Object detection is one of the important problems for autonomous robots . Faster R-CNN , one of the state-of-the-art object detection methods , approaches real time application ; nevertheless , computational time lies borderline of real time application , i.e. 5fps with VGG16 model in K40 GPU system in [ 1 ] . Moreover , computation time depends on model and image crop size , but precision is also affected ; usually , time and precision have trade-off relation . Class-imbalance in general machine learning has been widely studied , however , little attention has been paid on the subject of object detection . In this paper , we propose to explore and overcome such problem by application of several weighted variants of Cross Entropy loss , for examples Balanced Cross Entropy , Focal Loss and Class-Balanced Loss Based on Effective Number of Samples to our object detector . Experiments with BDD100 K ( a highly class-imbalanced driving database acquired from on-vehicle cameras capturing mostly Car-class objects and other minority object classes such as Bus , Person and Motor ) have proven better class-wise performances of detector trained with the afore-mentioned loss functions . In response to the fact that the probability is greater than or equal to the given threshold value , an object detection process is at least one of activated and maintained in an activated state . The object detection process detects target objects within an image representing at least one frame of a video image sequence an external environment . In response to the fact that the probability is below the given threshold , the object detection process is at least one of kept turned off and in a deactivated state . The image pickup apparatus controls a first mode for controlling a function to change an image pickup range of an image pickup unit in response to a received change request of the image pickup range to obtain image data of the changed image pickup range from the image pickup unit and a second mode for controlling an image process of a trimming and a magnification to the image data without performing the change of the image pickup range of the image pickup unit in response to the received change request of the image pickup range to generate image data of the image pickup range corresponding to the change request . The apparatus controls the change of the image pickup range of the image data in response to the change request of the image pickup range to select the first mode when an object detection process is not executed and select the second mode when the object detection process is executed . In an object detecting method according to an embodiment , external reference points are set in external space of a model of an object and an internal reference point is set in internal space of the model . A table is stored in which feature quantities on a local surface of the model are associated with positions of the external reference points and the internal reference point . One kind of object detection methods rely on region proposal , such as R - CNN [ reference ] , SPPnet [ reference ] , Fast R - CNN [ reference ] , Faster R - CNN [ reference ] , and R - FCN [ reference ] . Another family of object detectors do not rely on region proposal and directly estimate object candidates , such as SSD [ reference ] and YOLO [ reference ] . subsection : Accuracy and Efficiency Trade - offs We compare the accuracy - efficiency trade - offs of CornerNet - Lite with three state - of - the - art object detectors , including YOLOv3 , RetinaNet and CornerNet , on the validation set . The accuracy and efficiency trade - off curves are shown in Fig . Most state - of - the - art detection systems either classify some class - agnostic object proposals with CNN or directly regress object bounding boxes from a set of preset boxes ( e.g. anchor boxes ) . The architecture of our network inherits that of SSD , a recent object detection model . SSD proposed the idea of detecting objects on multiple feature layers with convolutional predictors . section : Related Work Object detection methods involve predicting object bounding boxes and categories . The work of R - CNN adopts region proposal methods ( , ) for producing multiple instance proposals , which are used for CNN - based classification . In this paper , the edge deterioration phenomenon is introduced in convolution neural network ( CNN ) to solve the problem that common CNN can not distinguish the specific weather . The proposed method used Mask R-CNN to extract the regions of interest including the foreground and foreground edges in the image , and superimposed them into the same-scale matrix and then input them into the network for classification . Outdoor traffic image experiments showed that this method can effectively improve the classification accuracy of the four weather conditions ( sunny , foggy , rainy and snowy ) . Our method operates in two-stages : keypoint estimation in frames or short clips , followed by lightweight tracking to generate keypoint predictions linked over the entire video . For frame-level pose estimation we experiment with Mask R-CNN , as well as our own proposed 3D extension of this model , which leverages temporal information over small clips to generate more robust frame predictions . We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark , PoseTrack , to validate various design choices of our model . Accurate detection and segmentation of densely-packed nuclei in images acquired under a variety of conditions is the major challenge . With a sufficient number of training examples , Mask R-CNN based methods have achieved state-of-the-art results on nuclei segmentation . However , the current pipeline requires fully-annotated training images , which are time-consuming to create and sometimes infeasible because of the noisy nature of microscopical images . Some recent attempts benefiting from Mask R-CNN formulate scene text detection task as an instance segmentation problem and achieve remarkable performance . In this paper , we present a new Mask R-CNN based framework named Pyramid Mask Text Detector ( PMTD ) to handle the scene text detection . Instead of binary text mask generated by the existing Mask R-CNN based methods , our PMTD performs pixel-level regression under the guidance of location-aware supervision , yielding a more informative soft text mask for each text instance . However , the mask quality , quantified as the IoU between the instance mask and its ground truth , is usually not well correlated with classification score . In this paper , we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks . The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score , and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation . To deal with uncertainty in labelling clothing , we introduce a new loss to utilize all available data which contain " maybe " labels . Detection+ has reduced about 2.54 % Normalized Error in FashionAI dataset and improved 3.2 % AP in human keypoints dataset coco2017 compared to the Mask R-CNN baseline . A large number of experimental results show the proposed approach achieves better results in different recognition datasets ( resp . , FashionAI , and Deepfashion ) with about ( resp . Instead , our framework is inspired by the clinical workflow ( Tanner-Whitehouse ) of hand bone age assessment , which focuses on the key components of the hand . The proposed framework is composed of two components : a Mask R-CNN subnet of pixelwise hand segmentation and a residual attention network for hand bone age assessment . The Mask R-CNN subnet segments the hands from X-ray images to avoid the distractions of other objects ( e.g. , X-ray tags ) . The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports , which is similar to the assessment procedure of clinicians . In this work , we show improvement to the existing convolutional neural network approach for a single-model neural text to speech synthesis with an extension to object segmentation features in a given scene . This model , built on top of a high effective and efficient building block of a trained neural network model ( masked R-CNN ) , generates as output , high precision images with bounding boxes and a significant audio signal quality improvement on the corresponding images detected in real time views . We show that a convolutional neural network model combined with neural TTS system can detect , classify and segment multiple objects in a single scene with their various bounding boxes , unique voices and display them in real life time . In order to solve the problem that the current driving warning method can not adapt to the unstructured road in open-pit mine , this paper proposes an early warning method that integrates target detection and obstacle distance threshold . Firstly , the original Mask R-CNN detection framework was improved according to the characteristics of open-pit mine obstacles , and dilated convolution was introduced into the framework network to expand the receptive field range without reducing the feature map to ensure the detection accuracy of larger targets . Then , a linear distance factor was constructed based on the target detection results to represent the depth information of obstacles in the input image , and an SVM warning model was established . As a concise and classic framework for object detection and instance segmentation , Mask R-CNN achieves promising performance in both two tasks . However , considering stronger feature representation for Mask R-CNN fashion framework , there is room for improvement from two aspects . On the one hand , performing multi-task prediction needs more credible feature extraction and multi-scale features integration to handle objects with varied scales . He et al . combined instance segmentation and keypoint prediction in their Mask - RCNN model . They append keypoint heads on top of RoI aligned feature maps to get a one - hot mask for each keypoint . Having observed this fact , we propose to segment instances in 3D point cloud instead of in 2D image or depth map . Similar to Mask - RCNN , which achieves instance segmentation by binary classification of pixels in image regions , we realize 3D instance segmentation using a PointNet - based network on point clouds in frustums . Based on 3D instance segmentation , we are able to achieve residual based 3D localization . Two loss functions , GHM - C and GHM - R are proposed to conquer the disharmony in classification and bounding box regression respectively . Experiments show that the collaborate with GHM , the performance of single - stage detector can easily surpass modern state - of - the - art two - stage detectors like FPN and Mask - RCNN with the same network backbone . Despite of the improvement of select uniform distribution to be the target , we still hold the opinion that the optimal distribution of gradient is hard to define and requires further research . Such a task was recently addressed in the Dense Regression ( DenseReg ) system of through a fully - convolutional network architecture . In this work , we introduce improved architectures by combining the DenseReg approach with the Mask - RCNN architecture , yielding our ‘ DensePose - RCNN ’ system . We develop cascaded extensions of DensePose - RCNN that further improve accuracy and describe a training - based interpolation method that allows us to turn a sparse supervision signal into a denser and more effective variant . In experiments , ARC-FCN shows very competitive results on the PASCAL VOC datasets , especially , it outperforms both Faster R-CNN [ 39 ] and R-FCN [ 8 ] with significantly better mean average precision ( mAP ) using larger value for the intersection-over-union ( IoU ) threshold ( i.e. , 0.7 in the experiments ) . ARC-FCN is still sufficiently efficient with a test-time speed of 380ms per image , faster than the Faster R-CNN but slower than the R-FCN . ∗Tianfu Wu is the corresponding author . With the use of Faster RCNN with VGG16 , pretrained on the ImageNet dataset , we achieve 88.3 mAP for a six object class X-ray detection problem . The use of R-FCN with ResNet-101 , yields 96.3 mAP for the two class firearm detection problem requiring 0.1 second computation per image . Overall we illustrate the comparative performance of these techniques as object localization strategies within cluttered X-ray security imagery . Therefore , we consider three main families of detectors : Faster Region-based Convolutional Neural Network ( Faster R-CNN ) , Region-based Fully Convolutional Network ( R-FCN ) , and Single Shot Multibox Detector ( SSD ) , which for the purpose of this work are called “ deep learning meta-architectures ” . We combine each of these meta-architectures with “ deep feature extractors ” such as VGG net and Residual Network ( ResNet ) . In this paper , we propose a novel algorithm to make full use of the facial features . The proposed algorithm is based on Region-based Fully Convolutional Network ( R-FCN ) with two improved parts for robust face detection , including the multi-scale training and a new feature-fusion scheme . Firstly , instead of utilizing fixed scales for all faces , we adopt multi-scale inputs to strengthen the features of the partial faces and increase the training set diversity . Region-based Fully ConvNet ( R-FCN ) designed for general object detection is difficult to be directly applied for pedestrian detection , due to being with large human pose and scale changes , and even with partial occlusion in surveillance scenarios . This paper presents a real time pedestrian detection method with partial occlusion handling , which builds on the framework of Region-based Fully ConvNet . Our method first training the noted deep model Region-based Fully Convolutional Networks ( R-FCN ) , and then an Adversarial Mask Dropout Network ( AMDN ) , which can generate occlusion features for training samples , is designed based on the trained R-FCN . Through the training strategy of adversarial learning between network R-FCN and network AMDN , the ability of network R-FCN to learn the features of occluded objects as well as its instance-level object detection performance is improved . Numerical experiments are conducted for instance detection to compare our proposed AGR-FCN with the original R-FCN on the self-made BHGI Database and the public database GMU Kitchen Dataset , which demonstrate that our proposed AGR-FCN outperforms original R-FCN and can achieve an average detection accuracy of nearly 90 % . Two main components of the recognition system are investigated : the color space of the input video and the network model of deep learning . We apply six color spaces ( RGB , normalized RGB , Ruta ’s RYG , YCbCr , HSV , and CIE Lab ) and three types of network models ( based on the Faster R-CNN and R-FCN models ) . All combinations of color spaces and network models are implemented and tested on a traffic light dataset with 1280 × 720 resolution . Particularly , We exploit several new techniques including position-sensitive average pooling , multi-scale training and testing and on-line hard example mining strategy to improve the detection accuracy . Over two most popular and challenging face detection benchmarks , FDDB and WIDER FACE , Face R-FCN achieves superior performance over state-of-the-arts . The networks are trained on the challenge free sequences of the CURE-TSD dataset . Our proposed network outperforms the state-of-the-art object detection networks , such as the Faster R-CNN inception Resnet V2 and R-FCN Resnet 101 , by a large margin and obtains a precision and recall of 94.60 % and 80.21 % , respectively , which is the current state of the art on this part of the dataset . In addition , the network is tested on the German Traffic Sign Detection Benchmark ( GTSDB ) dataset , where it achieves a precision and recall of 95.29 % and 89.01 % , respectively . The evaluation and comparison of these models include key metrics , such as the mean average precision ( mAP ) , memory allocation , running time , number of floating point operations , number of parameters of the model , and the effect of traffic sign image sizes . Our findings show that Faster R-CNN Inception Resnet V2 obtains the best mAP , while R-FCN Resnet 101 strikes the best trade-off between accuracy and execution time . YOLO V2 and SSD Mobilenet merit a special mention , in that the former achieves competitive accuracy results and is the second fastest detector , while the latter , is the fastest and the lightest model in terms of memory consumption , making it an optimal choice for deployment in mobile and embedded devices . An automatic road mark detection method based on local point cloud projection and 2D deep learning object detection is proposed . Firstly , the local ground point cloud regions are obtained by prior information ; Secondly , we perform orthogonal projection on the point cloud to project the point cloud into two-dimensional image by affine transformation and use R-FCN detection method to detect the road mark in the image . The holes on the image are filtered by local maximum filtering . We propose unsupervised Feature Propagation Generative Adversarial Network ( denoted as FPGAN ) for fast video object detection in this paper . In our video object detector , we detect objects on spare key frames using pre-trained state-of-the-art object detector R-FCN , and propagate CNN features to adjacent frames for fast detection via a light-weight transformation network . To learn the feature propagation network , we make full use of unlabeled video data and employ generative adversarial networks in model training . This method leverages a digital image capture system , pattern recognition , and motorized stages . Region-based convolutional neural network and template matching of known anatomical identifiable regions is used to find regions of interest ( e.g. , Bregma ) in rodents . For the detection of concealed weapon , multi-sensor stream data capturing framework is designed using sensor fusion techniques and also embedded with the feature extraction and segmentation of images module . Faster R-CNN ( Region-based Convolutional Neural Network ) model is trained for classification of weapons over collected dataset . Finally , several directions of work and tasks are provided as future work for the various research communities . With random orientation , various backgrounds as well as unpredictable noise make traditional image processing methods perform badly . In this paper , we propose using state-of-art Region-based fully convolutional networks to solve object detection tasks in aerial images . To make the whole system efficient we choose to utilize position-sensitive score maps which not only fully take advantage of the convolutional feature maps but also achieve a balance between translation-variance in object detection and translation-invariance in classification . This paper presents an effective single network for hand keypoint detection , instead of relying on the frequently-used two-stage pipeline consisting of localizing the hand and detecting the key points . Our method trains a fully convolutional neural network in an end-to-end manner , based on a novelly proposed pose anchor network , which can be deemed as an extension of the region proposal network ( RPN ) in Faster Region-based convolutional network . Moreover , we generate our pose anchor in a data-driven way , i.e. , a K-means cluster algorithm based on object keypoint similarity ( OKS ) , instead of manually design . Abstract A novel system for detection and classification of masses in breast mammography is introduced . The system integrates a breast segmentation module together with a modified region-based convolutional network to obtain detection and classification of masses according to BI-RADS score . While most of the previous work on mass identification in breast mammography has focused on classification , this study proposes to solve both the detection and the classification problems . In this article , a region-based Deep Convolutional Neural Network framework is presented for document structure learning . The contribution of this work involves efficient training of region based classifiers and effective ensembling for document image classification . The paper proposes a region-based deep learning convolutional neural network to detect objects within images able to identify the filamentary plasma structures that arise in the boundary region of the plasma in toroidal nuclear fusion reactors . The images required to train and test the neural model have been synthetically generated from statistical distributions , which reproduce the statistical properties in terms of position and intensity of experimental filaments . The gradient stream focuses on extracting structure features in details , and the image stream pays more attention to the information in intensity . In addition , to consider the locally non-uniform distribution of distortion in images , we add a region-based fully convolutional layer for using the information around the center of the input image patch . The final score of the overall image is calculated by averaging of the patch scores . Online multi-target tracking is an important prerequisite for real-time video sequence analysis . Because of low reliability in target detection , high tracking loss rate and unsmooth trajectory in online multi-target tracking , an online multi-target tracking model based on R-FCN ( region based fully convolutional networks ) network framework is proposed . Firstly , the target evaluation function based on R-FCN network framework is used to select more reliable candidates in the next frame between KF and detection results . This paper presents a model for airport detection using region-based fully convolutional neural networks . To achieve fast detection with high accuracy , we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units ( GPUs ) to speed up the training and testing time . [ reference ] , to avoid upscaling before feeding it into the network . In our architecture , we first apply a layer convolutional neural network directly to the LR image , and then apply a sub - pixel convolution layer that upscales the LR feature maps to produce . For a network composed of layers , the first layers can be described as follows : document : Training Region - based Object Detectors with Online Hard Example Mining The field of object detection has made significant advances riding on the wave of region - based ConvNets , but their training procedure still includes many heuristics and hyperparameters that are costly to tune . We present a simple yet surprisingly effective online hard example mining section : R - CNN and SPPnet The Region - based Convolutional Network method ( R - CNN ) [ reference ] achieves excellent object detection accuracy by using a deep ConvNet to classify object proposals . section : Introduction Recent advances in object detection are driven by the success of region proposal methods ( , ) and region - based convolutional neural networks ( R - CNNs ) . Although region - based CNNs were computationally expensive as originally developed in , their cost has been drastically reduced thanks to sharing convolutions across proposals . subsection : R - FCN Based Architecture R - FCN is a region - based fully convolutional network initially proposed for object detection . Unlike other region - based detectors such as Faster RCNN , R - FCN constructs a deeper fully convolutional network without increasing the speed overhead by shared computation on the entire image . Some more recent works have extended it to address various problems of detail . For example , the R - FCN [ reference ] proposed efficient region - wise fully convolutions without accuracy loss , to avoid the heavy region - wise CNN computations of the Faster - RCNN ; while the MS - CNN [ reference ] and FPN [ reference ] detect proposals at multiple output layers , so as to alleviate the scale mismatch between the RPN receptive fields and actual object size , for high - recall proposal detection . Alternatively , one - stage object detection architectures have also become popular , mostly due to their computational efficiency . To achieve reliability , it chooses the path which has the best reliability instead of considering dropping packets . Simulation results show that DETR has better performances in term of energy consumption , network lifetime and transmitting delay . Our approach streamlines the detection pipeline , effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task . The main ingredients of the new framework , called DEtection TRansformer or DETR , are a set-based global loss that forces unique predictions via bipartite matching , and a transformer encoder-decoder architecture . Given a fixed small set of learned object queries , DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel . The new model is conceptually simple and does not require a specialized library , unlike many other modern detectors . DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset . Moreover , DETR can be easily generalized to produce panoptic segmentation in a unified manner . We show that it significantly outperforms competitive baselines . Additionally , new linear detectors based on the zero gradient of the NML metrics are proposed . Applying various detection algorithms at the relay ( DetR ) and detection algorithms at the destination ( DetD ) , we present some DetR-DetD methods in MIMO DF relay channels . An appropriate DetR-DetD method can be employed according to the required error probability and detection complexity . The complexity analysis and simulation results validate the arguments of this paper . The invention provides a protective error detecting and verifying system of an electronic current transformer . The protective error detecting and verifying system comprises a standard current transformer which is connected with a protective error verifying device and a detected electronic current transformer ; the standard current transformer is connected with the protective error verifying device sequentially through a standard signal conversion device and a GPIB ( general purpose interface bus ) interface ; the detected electronic current transformer is connected with the protective error verifying device through a network adapter ; the protective error verifying device is used for collecting and calculating the output voltage of the standard current transformer and an output signal of the detected electronic current transformer , so that the error of the detected electronic current transformer can be corrected . Compared with the prior art , the protective error verifying system of the electronic current transformer can be used for overcoming the phase shifting problem of a Rogowski coil caused by the differential , and improving the protective error verifying precision of the electronic current transformer . This paper describes the development of a compact n/8 inductive voltage divider ( IVD ) for the use in the frequency range between 1 and 100 kHz and voltages up to 5 V. The development includes a number of additional inductive components for the bootstrap calibration setup such as a supply transformer which ensures the galvanic isolation of the calibration setup , a calibration transformer , and a detection transformer . The desired frequency and uncertainty range requires a two-stage IVD design with coaxial windings , a triaxial differential branch , and voltage controlled shields to minimize capacitive stray currents . We report on the measurement techniques and results of the equivalent inductance of current shunts at frequencies from 50 to 200 kHz . With a three-branch binary inductive current divider and difference detection transformer , the equivalent inductance of the cagelike shunts was measured against a set of four-terminal resistors whose time constants were accurately determined . The verification experiments for the systematic errors of the measurement setup and the phase comparator with the current ratio of 2 : 1 are also presented . The device comprises a base , wherein a first groove and a second groove which vertically intersect are formed in the base ; a sensing device for sensing an object is arranged at a part in which the first groove and the second groove intersect ; an electromagnet is arranged at one end of the first groove ; a driving device is arranged at one end of the second groove ; a control module is arranged in the base and correspondingly connected to the sensing device , the electromagnet and the driving device . With the adoption of the device , the problems in the prior art can be solved , such as manual placing of the network transformer on a detector , complex work and low efficiency ; the purpose of conveniently and quickly conveying each network transformer on the network transformer detector can be achieved . It has been preferred to develop an artificial bee algorithm to solve complex temperature optimization problems in power transformer windings realistically and quickly . In this paper , the power transformer detection panel has been developed . The data required for the algorithm have been obtained by performing experiments in different power transformers .
Object detection is one of the important problems for autonomous robots . Faster R-CNN , one of the state-of-the-art object detection methods , approaches real time application ; nevertheless , computational time lies borderline of real time application , i.e. 5fps with VGG16 model in K40 GPU system in [ 1 ] . Moreover , computation time depends on model and image crop size , but precision is also affected ; usually , time and precision have trade-off relation .
Class-imbalance in general machine learning has been widely studied , however , little attention has been paid on the subject of object detection . In this paper , we propose to explore and overcome such problem by application of several weighted variants of Cross Entropy loss , for examples Balanced Cross Entropy , Focal Loss and Class-Balanced Loss Based on Effective Number of Samples to our object detector . Experiments with BDD100 K ( a highly class-imbalanced driving database acquired from on-vehicle cameras capturing mostly Car-class objects and other minority object classes such as Bus , Person and Motor ) have proven better class-wise performances of detector trained with the afore-mentioned loss functions .
In response to the fact that the probability is greater than or equal to the given threshold value , an object detection process is at least one of activated and maintained in an activated state . The object detection process detects target objects within an image representing at least one frame of a video image sequence an external environment . In response to the fact that the probability is below the given threshold , the object detection process is at least one of kept turned off and in a deactivated state .
The image pickup apparatus controls a first mode for controlling a function to change an image pickup range of an image pickup unit in response to a received change request of the image pickup range to obtain image data of the changed image pickup range from the image pickup unit and a second mode for controlling an image process of a trimming and a magnification to the image data without performing the change of the image pickup range of the image pickup unit in response to the received change request of the image pickup range to generate image data of the image pickup range corresponding to the change request . The apparatus controls the change of the image pickup range of the image data in response to the change request of the image pickup range to select the first mode when an object detection process is not executed and select the second mode when the object detection process is executed .
In an object detecting method according to an embodiment , external reference points are set in external space of a model of an object and an internal reference point is set in internal space of the model . A table is stored in which feature quantities on a local surface of the model are associated with positions of the external reference points and the internal reference point .
One kind of object detection methods rely on region proposal , such as R - CNN [ reference ] , SPPnet [ reference ] , Fast R - CNN [ reference ] , Faster R - CNN [ reference ] , and R - FCN [ reference ] . Another family of object detectors do not rely on region proposal and directly estimate object candidates , such as SSD [ reference ] and YOLO [ reference ] .
subsection : Accuracy and Efficiency Trade - offs We compare the accuracy - efficiency trade - offs of CornerNet - Lite with three state - of - the - art object detectors , including YOLOv3 , RetinaNet and CornerNet , on the validation set . The accuracy and efficiency trade - off curves are shown in Fig .
Most state - of - the - art detection systems either classify some class - agnostic object proposals with CNN or directly regress object bounding boxes from a set of preset boxes ( e.g. anchor boxes ) . The architecture of our network inherits that of SSD , a recent object detection model . SSD proposed the idea of detecting objects on multiple feature layers with convolutional predictors .
section : Related Work Object detection methods involve predicting object bounding boxes and categories . The work of R - CNN adopts region proposal methods ( , ) for producing multiple instance proposals , which are used for CNN - based classification .
In this paper , the edge deterioration phenomenon is introduced in convolution neural network ( CNN ) to solve the problem that common CNN can not distinguish the specific weather . The proposed method used Mask R-CNN to extract the regions of interest including the foreground and foreground edges in the image , and superimposed them into the same-scale matrix and then input them into the network for classification . Outdoor traffic image experiments showed that this method can effectively improve the classification accuracy of the four weather conditions ( sunny , foggy , rainy and snowy ) .
Our method operates in two-stages : keypoint estimation in frames or short clips , followed by lightweight tracking to generate keypoint predictions linked over the entire video . For frame-level pose estimation we experiment with Mask R-CNN , as well as our own proposed 3D extension of this model , which leverages temporal information over small clips to generate more robust frame predictions . We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark , PoseTrack , to validate various design choices of our model .
Accurate detection and segmentation of densely-packed nuclei in images acquired under a variety of conditions is the major challenge . With a sufficient number of training examples , Mask R-CNN based methods have achieved state-of-the-art results on nuclei segmentation . However , the current pipeline requires fully-annotated training images , which are time-consuming to create and sometimes infeasible because of the noisy nature of microscopical images .
Some recent attempts benefiting from Mask R-CNN formulate scene text detection task as an instance segmentation problem and achieve remarkable performance . In this paper , we present a new Mask R-CNN based framework named Pyramid Mask Text Detector ( PMTD ) to handle the scene text detection . Instead of binary text mask generated by the existing Mask R-CNN based methods , our PMTD performs pixel-level regression under the guidance of location-aware supervision , yielding a more informative soft text mask for each text instance .
However , the mask quality , quantified as the IoU between the instance mask and its ground truth , is usually not well correlated with classification score . In this paper , we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks . The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score , and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation .
To deal with uncertainty in labelling clothing , we introduce a new loss to utilize all available data which contain " maybe " labels . Detection+ has reduced about 2.54 % Normalized Error in FashionAI dataset and improved 3.2 % AP in human keypoints dataset coco2017 compared to the Mask R-CNN baseline . A large number of experimental results show the proposed approach achieves better results in different recognition datasets ( resp . , FashionAI , and Deepfashion ) with about ( resp .
Instead , our framework is inspired by the clinical workflow ( Tanner-Whitehouse ) of hand bone age assessment , which focuses on the key components of the hand . The proposed framework is composed of two components : a Mask R-CNN subnet of pixelwise hand segmentation and a residual attention network for hand bone age assessment . The Mask R-CNN subnet segments the hands from X-ray images to avoid the distractions of other objects ( e.g. , X-ray tags ) . The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports , which is similar to the assessment procedure of clinicians .
In this work , we show improvement to the existing convolutional neural network approach for a single-model neural text to speech synthesis with an extension to object segmentation features in a given scene . This model , built on top of a high effective and efficient building block of a trained neural network model ( masked R-CNN ) , generates as output , high precision images with bounding boxes and a significant audio signal quality improvement on the corresponding images detected in real time views . We show that a convolutional neural network model combined with neural TTS system can detect , classify and segment multiple objects in a single scene with their various bounding boxes , unique voices and display them in real life time .
In order to solve the problem that the current driving warning method can not adapt to the unstructured road in open-pit mine , this paper proposes an early warning method that integrates target detection and obstacle distance threshold . Firstly , the original Mask R-CNN detection framework was improved according to the characteristics of open-pit mine obstacles , and dilated convolution was introduced into the framework network to expand the receptive field range without reducing the feature map to ensure the detection accuracy of larger targets . Then , a linear distance factor was constructed based on the target detection results to represent the depth information of obstacles in the input image , and an SVM warning model was established .
As a concise and classic framework for object detection and instance segmentation , Mask R-CNN achieves promising performance in both two tasks . However , considering stronger feature representation for Mask R-CNN fashion framework , there is room for improvement from two aspects . On the one hand , performing multi-task prediction needs more credible feature extraction and multi-scale features integration to handle objects with varied scales .
He et al . combined instance segmentation and keypoint prediction in their Mask - RCNN model . They append keypoint heads on top of RoI aligned feature maps to get a one - hot mask for each keypoint .
Having observed this fact , we propose to segment instances in 3D point cloud instead of in 2D image or depth map . Similar to Mask - RCNN , which achieves instance segmentation by binary classification of pixels in image regions , we realize 3D instance segmentation using a PointNet - based network on point clouds in frustums . Based on 3D instance segmentation , we are able to achieve residual based 3D localization .
Two loss functions , GHM - C and GHM - R are proposed to conquer the disharmony in classification and bounding box regression respectively . Experiments show that the collaborate with GHM , the performance of single - stage detector can easily surpass modern state - of - the - art two - stage detectors like FPN and Mask - RCNN with the same network backbone . Despite of the improvement of select uniform distribution to be the target , we still hold the opinion that the optimal distribution of gradient is hard to define and requires further research .
Such a task was recently addressed in the Dense Regression ( DenseReg ) system of through a fully - convolutional network architecture . In this work , we introduce improved architectures by combining the DenseReg approach with the Mask - RCNN architecture , yielding our ‘ DensePose - RCNN ’ system . We develop cascaded extensions of DensePose - RCNN that further improve accuracy and describe a training - based interpolation method that allows us to turn a sparse supervision signal into a denser and more effective variant .
In experiments , ARC-FCN shows very competitive results on the PASCAL VOC datasets , especially , it outperforms both Faster R-CNN [ 39 ] and R-FCN [ 8 ] with significantly better mean average precision ( mAP ) using larger value for the intersection-over-union ( IoU ) threshold ( i.e. , 0.7 in the experiments ) . ARC-FCN is still sufficiently efficient with a test-time speed of 380ms per image , faster than the Faster R-CNN but slower than the R-FCN . ∗Tianfu Wu is the corresponding author .
With the use of Faster RCNN with VGG16 , pretrained on the ImageNet dataset , we achieve 88.3 mAP for a six object class X-ray detection problem . The use of R-FCN with ResNet-101 , yields 96.3 mAP for the two class firearm detection problem requiring 0.1 second computation per image . Overall we illustrate the comparative performance of these techniques as object localization strategies within cluttered X-ray security imagery .
Therefore , we consider three main families of detectors : Faster Region-based Convolutional Neural Network ( Faster R-CNN ) , Region-based Fully Convolutional Network ( R-FCN ) , and Single Shot Multibox Detector ( SSD ) , which for the purpose of this work are called “ deep learning meta-architectures ” . We combine each of these meta-architectures with “ deep feature extractors ” such as VGG net and Residual Network ( ResNet ) .
In this paper , we propose a novel algorithm to make full use of the facial features . The proposed algorithm is based on Region-based Fully Convolutional Network ( R-FCN ) with two improved parts for robust face detection , including the multi-scale training and a new feature-fusion scheme . Firstly , instead of utilizing fixed scales for all faces , we adopt multi-scale inputs to strengthen the features of the partial faces and increase the training set diversity .
Region-based Fully ConvNet ( R-FCN ) designed for general object detection is difficult to be directly applied for pedestrian detection , due to being with large human pose and scale changes , and even with partial occlusion in surveillance scenarios . This paper presents a real time pedestrian detection method with partial occlusion handling , which builds on the framework of Region-based Fully ConvNet .
Our method first training the noted deep model Region-based Fully Convolutional Networks ( R-FCN ) , and then an Adversarial Mask Dropout Network ( AMDN ) , which can generate occlusion features for training samples , is designed based on the trained R-FCN . Through the training strategy of adversarial learning between network R-FCN and network AMDN , the ability of network R-FCN to learn the features of occluded objects as well as its instance-level object detection performance is improved . Numerical experiments are conducted for instance detection to compare our proposed AGR-FCN with the original R-FCN on the self-made BHGI Database and the public database GMU Kitchen Dataset , which demonstrate that our proposed AGR-FCN outperforms original R-FCN and can achieve an average detection accuracy of nearly 90 % .
Two main components of the recognition system are investigated : the color space of the input video and the network model of deep learning . We apply six color spaces ( RGB , normalized RGB , Ruta ’s RYG , YCbCr , HSV , and CIE Lab ) and three types of network models ( based on the Faster R-CNN and R-FCN models ) . All combinations of color spaces and network models are implemented and tested on a traffic light dataset with 1280 × 720 resolution .
Particularly , We exploit several new techniques including position-sensitive average pooling , multi-scale training and testing and on-line hard example mining strategy to improve the detection accuracy . Over two most popular and challenging face detection benchmarks , FDDB and WIDER FACE , Face R-FCN achieves superior performance over state-of-the-arts .
The networks are trained on the challenge free sequences of the CURE-TSD dataset . Our proposed network outperforms the state-of-the-art object detection networks , such as the Faster R-CNN inception Resnet V2 and R-FCN Resnet 101 , by a large margin and obtains a precision and recall of 94.60 % and 80.21 % , respectively , which is the current state of the art on this part of the dataset . In addition , the network is tested on the German Traffic Sign Detection Benchmark ( GTSDB ) dataset , where it achieves a precision and recall of 95.29 % and 89.01 % , respectively .
The evaluation and comparison of these models include key metrics , such as the mean average precision ( mAP ) , memory allocation , running time , number of floating point operations , number of parameters of the model , and the effect of traffic sign image sizes . Our findings show that Faster R-CNN Inception Resnet V2 obtains the best mAP , while R-FCN Resnet 101 strikes the best trade-off between accuracy and execution time . YOLO V2 and SSD Mobilenet merit a special mention , in that the former achieves competitive accuracy results and is the second fastest detector , while the latter , is the fastest and the lightest model in terms of memory consumption , making it an optimal choice for deployment in mobile and embedded devices .
An automatic road mark detection method based on local point cloud projection and 2D deep learning object detection is proposed . Firstly , the local ground point cloud regions are obtained by prior information ; Secondly , we perform orthogonal projection on the point cloud to project the point cloud into two-dimensional image by affine transformation and use R-FCN detection method to detect the road mark in the image . The holes on the image are filtered by local maximum filtering .
We propose unsupervised Feature Propagation Generative Adversarial Network ( denoted as FPGAN ) for fast video object detection in this paper . In our video object detector , we detect objects on spare key frames using pre-trained state-of-the-art object detector R-FCN , and propagate CNN features to adjacent frames for fast detection via a light-weight transformation network . To learn the feature propagation network , we make full use of unlabeled video data and employ generative adversarial networks in model training .
This method leverages a digital image capture system , pattern recognition , and motorized stages . Region-based convolutional neural network and template matching of known anatomical identifiable regions is used to find regions of interest ( e.g. , Bregma ) in rodents .
For the detection of concealed weapon , multi-sensor stream data capturing framework is designed using sensor fusion techniques and also embedded with the feature extraction and segmentation of images module . Faster R-CNN ( Region-based Convolutional Neural Network ) model is trained for classification of weapons over collected dataset . Finally , several directions of work and tasks are provided as future work for the various research communities .
With random orientation , various backgrounds as well as unpredictable noise make traditional image processing methods perform badly . In this paper , we propose using state-of-art Region-based fully convolutional networks to solve object detection tasks in aerial images . To make the whole system efficient we choose to utilize position-sensitive score maps which not only fully take advantage of the convolutional feature maps but also achieve a balance between translation-variance in object detection and translation-invariance in classification .
This paper presents an effective single network for hand keypoint detection , instead of relying on the frequently-used two-stage pipeline consisting of localizing the hand and detecting the key points . Our method trains a fully convolutional neural network in an end-to-end manner , based on a novelly proposed pose anchor network , which can be deemed as an extension of the region proposal network ( RPN ) in Faster Region-based convolutional network . Moreover , we generate our pose anchor in a data-driven way , i.e. , a K-means cluster algorithm based on object keypoint similarity ( OKS ) , instead of manually design .
Abstract A novel system for detection and classification of masses in breast mammography is introduced . The system integrates a breast segmentation module together with a modified region-based convolutional network to obtain detection and classification of masses according to BI-RADS score . While most of the previous work on mass identification in breast mammography has focused on classification , this study proposes to solve both the detection and the classification problems .
In this article , a region-based Deep Convolutional Neural Network framework is presented for document structure learning . The contribution of this work involves efficient training of region based classifiers and effective ensembling for document image classification .
The paper proposes a region-based deep learning convolutional neural network to detect objects within images able to identify the filamentary plasma structures that arise in the boundary region of the plasma in toroidal nuclear fusion reactors . The images required to train and test the neural model have been synthetically generated from statistical distributions , which reproduce the statistical properties in terms of position and intensity of experimental filaments .
The gradient stream focuses on extracting structure features in details , and the image stream pays more attention to the information in intensity . In addition , to consider the locally non-uniform distribution of distortion in images , we add a region-based fully convolutional layer for using the information around the center of the input image patch . The final score of the overall image is calculated by averaging of the patch scores .
Online multi-target tracking is an important prerequisite for real-time video sequence analysis . Because of low reliability in target detection , high tracking loss rate and unsmooth trajectory in online multi-target tracking , an online multi-target tracking model based on R-FCN ( region based fully convolutional networks ) network framework is proposed . Firstly , the target evaluation function based on R-FCN network framework is used to select more reliable candidates in the next frame between KF and detection results .
This paper presents a model for airport detection using region-based fully convolutional neural networks . To achieve fast detection with high accuracy , we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units ( GPUs ) to speed up the training and testing time .
[ reference ] , to avoid upscaling before feeding it into the network . In our architecture , we first apply a layer convolutional neural network directly to the LR image , and then apply a sub - pixel convolution layer that upscales the LR feature maps to produce . For a network composed of layers , the first layers can be described as follows :
document : Training Region - based Object Detectors with Online Hard Example Mining The field of object detection has made significant advances riding on the wave of region - based ConvNets , but their training procedure still includes many heuristics and hyperparameters that are costly to tune . We present a simple yet surprisingly effective online hard example mining
section : R - CNN and SPPnet The Region - based Convolutional Network method ( R - CNN ) [ reference ] achieves excellent object detection accuracy by using a deep ConvNet to classify object proposals .
section : Introduction Recent advances in object detection are driven by the success of region proposal methods ( , ) and region - based convolutional neural networks ( R - CNNs ) . Although region - based CNNs were computationally expensive as originally developed in , their cost has been drastically reduced thanks to sharing convolutions across proposals .
subsection : R - FCN Based Architecture R - FCN is a region - based fully convolutional network initially proposed for object detection . Unlike other region - based detectors such as Faster RCNN , R - FCN constructs a deeper fully convolutional network without increasing the speed overhead by shared computation on the entire image .
Some more recent works have extended it to address various problems of detail . For example , the R - FCN [ reference ] proposed efficient region - wise fully convolutions without accuracy loss , to avoid the heavy region - wise CNN computations of the Faster - RCNN ; while the MS - CNN [ reference ] and FPN [ reference ] detect proposals at multiple output layers , so as to alleviate the scale mismatch between the RPN receptive fields and actual object size , for high - recall proposal detection . Alternatively , one - stage object detection architectures have also become popular , mostly due to their computational efficiency .
To achieve reliability , it chooses the path which has the best reliability instead of considering dropping packets . Simulation results show that DETR has better performances in term of energy consumption , network lifetime and transmitting delay .
Our approach streamlines the detection pipeline , effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task . The main ingredients of the new framework , called DEtection TRansformer or DETR , are a set-based global loss that forces unique predictions via bipartite matching , and a transformer encoder-decoder architecture . Given a fixed small set of learned object queries , DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel . The new model is conceptually simple and does not require a specialized library , unlike many other modern detectors . DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset . Moreover , DETR can be easily generalized to produce panoptic segmentation in a unified manner . We show that it significantly outperforms competitive baselines .
Additionally , new linear detectors based on the zero gradient of the NML metrics are proposed . Applying various detection algorithms at the relay ( DetR ) and detection algorithms at the destination ( DetD ) , we present some DetR-DetD methods in MIMO DF relay channels . An appropriate DetR-DetD method can be employed according to the required error probability and detection complexity . The complexity analysis and simulation results validate the arguments of this paper .
The invention provides a protective error detecting and verifying system of an electronic current transformer . The protective error detecting and verifying system comprises a standard current transformer which is connected with a protective error verifying device and a detected electronic current transformer ; the standard current transformer is connected with the protective error verifying device sequentially through a standard signal conversion device and a GPIB ( general purpose interface bus ) interface ; the detected electronic current transformer is connected with the protective error verifying device through a network adapter ; the protective error verifying device is used for collecting and calculating the output voltage of the standard current transformer and an output signal of the detected electronic current transformer , so that the error of the detected electronic current transformer can be corrected . Compared with the prior art , the protective error verifying system of the electronic current transformer can be used for overcoming the phase shifting problem of a Rogowski coil caused by the differential , and improving the protective error verifying precision of the electronic current transformer .
This paper describes the development of a compact n/8 inductive voltage divider ( IVD ) for the use in the frequency range between 1 and 100 kHz and voltages up to 5 V. The development includes a number of additional inductive components for the bootstrap calibration setup such as a supply transformer which ensures the galvanic isolation of the calibration setup , a calibration transformer , and a detection transformer . The desired frequency and uncertainty range requires a two-stage IVD design with coaxial windings , a triaxial differential branch , and voltage controlled shields to minimize capacitive stray currents .
We report on the measurement techniques and results of the equivalent inductance of current shunts at frequencies from 50 to 200 kHz . With a three-branch binary inductive current divider and difference detection transformer , the equivalent inductance of the cagelike shunts was measured against a set of four-terminal resistors whose time constants were accurately determined . The verification experiments for the systematic errors of the measurement setup and the phase comparator with the current ratio of 2 : 1 are also presented .
The device comprises a base , wherein a first groove and a second groove which vertically intersect are formed in the base ; a sensing device for sensing an object is arranged at a part in which the first groove and the second groove intersect ; an electromagnet is arranged at one end of the first groove ; a driving device is arranged at one end of the second groove ; a control module is arranged in the base and correspondingly connected to the sensing device , the electromagnet and the driving device . With the adoption of the device , the problems in the prior art can be solved , such as manual placing of the network transformer on a detector , complex work and low efficiency ; the purpose of conveniently and quickly conveying each network transformer on the network transformer detector can be achieved .
It has been preferred to develop an artificial bee algorithm to solve complex temperature optimization problems in power transformer windings realistically and quickly . In this paper , the power transformer detection panel has been developed . The data required for the algorithm have been obtained by performing experiments in different power transformers .
Comparison of faster R-CNN models for object detection Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses Optimizing the recognition of objects in images And a control method for the imaging apparatus Object detecting method and object detecting device R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection CornerNet-Lite: Efficient Keypoint Based Object Detection Detecting Oriented Text in Natural Images by Linking Segments Instance-Aware Semantic Segmentation via Multi-task Network Cascades Weather Recognition Based on Edge Deterioration and Convolutional Neural Networks Detect-and-Track: Efficient Pose Estimation in Videos Robust Nucleus Detection from Microscopical Image with Partially Labeled Exemplars Pyramid Mask Text Detector Mask Scoring R-CNN Clothes Keypoints Localization and Attribute Recognition via Prior Knowledge Residual Attention Based Network for Hand Bone Age Assessment Deep Learning-Based Scene Image Detection and Segmentation with Speech Synthesis in Real Time An open-pit mine roadway obstacle warning method integrating the object detection and distance threshold model Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Frustum PointNets for 3D Object Detection from RGB-D Data Gradient Harmonized Single-stage Detector DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild Object Detection via End-to-End Integration of Aspect Ratio and Context Aware Part-based Models and Fully Convolutional Networks An evaluation of region based object detection strategies within X-ray baggage security imagery A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition Highly Occluded Face Detection: An Improved R-FCN Approach Real- Time Pedestrian Detection in Crowded Scenes Using Deep Omega-Shape Features AGR-FCN: Adversarial Generated Region based on Fully Convolutional Networks for Single- and Multiple-Instance Object Detection An Efficient Color Space for Deep-Learning Based Traffic Light Recognition Detecting Faces Using Region-based Fully Convolutional Networks Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint Evaluation of deep neural networks for traffic sign detection systems Road mark detection based on local point cloud projection and R-FCN Unsupervised Feature Propagation for Fast Video Object Detection Using Generative Adversarial Networks Precision Robotic-Assisted Implantation for Preclinical StereotacticNeurosurgery ADoCW: An Automated method for Detection of Concealed Weapon Robust object detection for tiny and dense targets in VHR aerial images Pose Anchor: A Single-Stage Hand Keypoint Detection Network A CNN based method for automatic mass detection and classification in mammograms Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks Convolutional Neural Networks for the Identification of Filaments from Fast Visual Imaging Cameras in Tokamak Reactors Two-Stream Convolutional Networks for Blind Image Quality Assessment Multi-candidate association online multi-target tracking based on R-FCN framework Efficient airport detection using region-based fully convolutional neural networks Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Training Region-Based Object Detectors with Online Hard Example Mining Fast R-CNN Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Detecting Faces Using Region-based Fully Convolutional Networks Cascade R-CNN: Delving Into High Quality Object Detection Reliability considered routing protocol in Wireless Sensor Networks End-to-End Object Detection with Transformers Deep Learning Detection Networks in MIMO Decode-Forward Relay Channels Protective error detecting and verifying system of electronic current transformer Two-Stage Coaxial Inductive Voltage Divider and Its Calibration Setup for Use Between 1 and 100 kHz Determination of Equivalent Inductance of Current Shunts at Frequency Up to 200 kHz Automatic feeding device for network transformer Temperature Analysis in Power Transformer Windings Using Created Artificial Bee Algorithm and Computer Program
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ArXiv ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICPR CVPR ICME ICCV Workshops ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition AAAI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICIP ICONIP (6) ICASSP IST MMM (1) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE International Conference on Computer Vision (ICCV) IEEE Transactions on Pattern Analysis and Machine Intelligence ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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