In this paper , we propose a novel object detection approach based on the Multiscale-SELU-DenseNet ( MSE-DenseNet ) and the dynamic anchor assignment ( DAA ) strategy . We fuse a highly modified DenseNet and a feature pyramid network into the MSE-DenseNet to extract multi-level feature descriptions of RS images , and then we use them to detect objects on various scales . To further improve the detection performance , we propose a DAA strategy for assigning prior anchor boxes . It can learn half and reusing half of the preceding feature maps , and has fewer parameters . Semantic aggregation adopts the Feature Pyramid Network ( FPN ) into the original SSD . Adding more semantic information to each scale to detect different size objects . We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder ( VFE ) of different scales at point-wise level and project into multiple pseudo-image feature maps . We further propose an attentive voxel feature encoding that outperforms plain VFE and a feature fusion pyramid network to aggregate multi-scale information at feature map level . Experiments on the KITTI benchmark show that a single HVNet achieves the best mAP among all existing methods with a real time inference speed of 31Hz . In other words , we reuse the lost data from the pooling layers and concatenate that data as extra feature maps to contribute to classification and detection . Meanwhile , in the feature pyramid , we concatenate feature maps of the lower layers and feature maps that are deconvolved from upper layers to make explicit relationships between layers and to effectively increase the number of channels . The results show that our enhanced SSD for gastric polyp detection can realize real-time polyp detection with 50 frames per second ( FPS ) and can improve the mean average precision ( mAP ) from 88.5 % to 90.4 % , with only a little loss in time-performance . Focusing on these challenges , this paper proposes an end-to-end framework called the multiple feature pyramid network ( MFPN ) . In MFPN , we design an effective feature pyramid and a tailored pyramid pooling module , taking advantage of multilevel semantic features of high-resolution remote sensing images . In the optimization stage , a weighted balance loss function is presented to solve the class imbalance problem caused by the sparseness of roads . We propose a flexible Attribute Localization Module ( ALM ) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels . Moreover , a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance . The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision . The low-level features have high resolutions but could not represent the features well . The feature pyramid structure contains multi-scale features . To combine the high and low-level features of the pyramid , the algorithm of this paper applied the deconvolution network to the high-level features of the feature pyramid to get the semantic information , dilated convolution network to learn the position information of the low-level features and used convolution for the middle level features to reduce the feature channels , then used convolution to fuse the features . However , the current designs for feature pyramids are still inefficient to integrate the semantic information over different scales . In this paper , we begin by investigating current feature pyramids solutions , and then reformulate the feature pyramid construction as the feature reconfiguration process . Finally , we propose a novel reconfiguration architecture to combine low-level representations with high-level semantic features in a highly-nonlinear yet efficient way . The standard pipeline in pedestrian detection is sliding a pedestrian model on an image feature pyramid to detect pedestrians of different scales . In this pipeline , feature pyramid construction is time consuming and becomes the bottleneck for fast detection . Recently , a method called multiresolution filtered channels ( MRFC ) was proposed which only used single scale feature maps to achieve fast detection . We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation . Specifically , given two input frames , we forward-warp the frames and their feature pyramid representations based on an optical flow estimate using softmax splatting . In doing so , the softmax splatting seamlessly handles cases where multiple source pixels map to the same target location . This is achieved by : ( a ) predicting category-sensitive semantic maps for the object existing possibility , and ( b ) producing category-agnostic bounding box for each position that potentially contains an object . The scales of target boxes are naturally associated with feature pyramid representations for each input image . Without bells and whistles , FoveaBox achieves state-of-the-art single model performance of 42.1 AP on the standard COCO detection benchmark . We show how scalable operators can be explicitly constructed using a Gaussian neighbourhood function . We extend this approach to achieve an efficient approximation via a feature map pyramid that implicitly embodies operator scaling . In both cases we provide performance evaluation with respect to edge localisation . This paper proposes a fast pixel-wise labeling method called scanning convolutional network ( SCN ) for mudflat aquaculture area detection with infrared remote sensing images . SCN improves the traditional fully convolutional network ( FCN ) by replacing convolution layers with scanning convolution modules ( SCM ) and a feature pyramid design , which simultaneously learns large scale sea-land environmental features and mudflat structure details with less computational costs . A set of Landsat-8 satellite images , with three visible bands and three infrared bands , are used to evaluate the proposed method . Within person search problem , existing pedestrian detectors usually generate person bounding boxes with different sizes , which will degrade the performance of person re-identification in matching process . The built-in feature pyramid hierarchy to address the multi-scale matching problem adopted in previous studies , is prone to be effective for reducing the semantic gaps among different level features . Therefore , in this paper we propose an Adaptive Feature Pyramid (AFP)-based multi-scale matching method for person search . To achieve a better balance of accuracy and computational complexity for optical flow estimation , a Structural Pyramid Network ( StruPyNet ) is designed to combine structural pyramid processing and feature pyramid processing . In order to efficiently distribute parameters and computations among all structural pyramid levels , the proposed StruPyNet flexibly cascades more small flow estimators at lower structural pyramid levels and less small flow estimators at higher structural pyramid levels . On the other hand , deep layers obtain more semantic information but the spatial resolution is compromised . Recent work like Feature Pyramid Network ( FPN ) proposes a divide and conquer principle . A U - shape structure is attached to maintain both the high spatial resolution and semantic information . The Faster R - CNN method further proposes the region proposal networks to generate bounding boxes with various scales . Based on the Faster R - CNN , the FPN approach introduces feature pyramid to extract features with different scales from a single image . The SSD method utilizes feature maps from different stages to process visual information at different scales . How important are lateral connections ? Table 1 ( e ) shows the ablation results of a top - down feature pyramid without the 1×1 lateral connections . This top - down pyramid has strong semantic features and fine resolutions . In this work , we present a robust legalization algorithm for standard cell placement that minimizes maximum cell movements fast and effectively based on a novel network-flow approach . The idea is inspired by path augmentation but with important differences . In contrast to the classical path augmentation approaches , we resolve bin overflows by finding several candidate paths that guarantee realizable ( legal ) flow solutions . More specifically , the contribution of the proposed method mainly consists of three components : Augmented Feature Pyramid networks , Self-adaptive Feature Fusion and a Dependencies Computation ( DC ) Module . A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly . Then , the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions . Therefore , a more powerful hierarchical representation structure can be obtained by effectively aggregating multi-level contexts . We apply Rev-Dense FPN on SSD framework , which reaches 81.1 % mAP ( mean average precision ) on the PASCAL VOC 2007 dataset and 31.2 AP on the MS COCO dataset . The results show that Rev-Dense FPN is more effective in dealing with diversified object sizes . We show how the FPN model can be used for formal specification and verification of digital systems . The consistent FPN model is actually a state machine , from which we can obtain a consistent marked Petri net ( MPN ) model . Based on the consistent MPN model , the hardware prototype at register transfer level can be easily induced by using the optimization rules . Fully convolutional neural network ( FCN ) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels , which boiled down all the redundant calculation , and most recent state-of-the-art methods such as Faster-RCNN , SSD , YOLO and FPN use FCN as their backbone . So here comes one question : Can we find a universal strategy to further accelerate FCN with higher accuracy , so could accelerate all the recent FCN-based methods ? However , no systematic and comprehensive review has been provided for FPNs as knowledge representation formalisms . Giving this evolving research area , this work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms , knowledge representations and FPN models . In addition , we provide a survey of the applications of FPNs for solving practical problems in variety of fields . In order to eliminate the fixed-pattern noise ( FPN ) in the output image of time-delay-integration CMOS image sensor ( TDI-CIS ) , a FPN correction method based on gray value compensation is proposed . One hundred images are first captured under uniform illumination . Then the reliability of the multi-state system was determined . Finally , with respect to the different Type 1 importance measures , according to the state probability , multi-state system , the reliability of the state with fuzzy petri nets ( FPN ) composite importance measures ( CIM ) was estimated and the comparison results table between the CIM and the FPN CIM was produced . From the experiment , multi-state system reliability with composite importance measures of fuzzy petri nets in this paper shows that the variances are consistently low , and the measures can be considered to be robust . The proposed NUC technique combines the advantages of global- and local-based correction methods , thus can effectively eliminate column FPN without losing original thermal details . The performance of the proposed method is systematically evaluated , and is compared with the state-of-the-art column FPN correction solutions using realistic infrared images . We show how a simple convolutional neural network ( CNN ) can be trained to accurately and robustly regress 6 degrees of freedom ( 6DoF ) 3D head pose , directly from image intensities . We further explain how this FacePoseNet ( FPN ) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks . We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments . There is a consistent superiority compared to the architectures searched on ImageNet classification . Our main result architecture achieves better performance than ResNet-101 on COCO with the FPN detector . In addition , we illustrate the architectures searched by DetNAS and find some meaningful patterns . FPN uses a top - down architecture with lateral connections to build an in - network feature pyramid from a single - scale input . Faster R - CNN with an FPN backbone extracts RoI features from different levels of the feature pyramid according to their scale , but otherwise the rest of the approach is similar to vanilla ResNet . Using a ResNet - FPN backbone for feature extraction with Mask R - CNN gives excellent gains in both accuracy and speed . Segmentation is a pixel - to - pixel task and we exploit the spatial layout of masks by using an FCN . In Table 2e , we compare multi - layer perceptrons ( MLP ) and FCNs , using a ResNet - 50 - FPN backbone . Using FCNs gives a 2.1 mask AP gain over MLPs . For this result , even though the full Mask R - CNN model is trained , only the classification and box outputs are used at inference ( the mask output is ignored ) . Mask R - CNN using ResNet - 101 - FPN outperforms the base variants of all previous state - ofthe - art models , including the single - model variant of G - RMI [ reference ] , the winner of the COCO 2016 Detection Challenge . Using ResNeXt - 101 - FPN , Mask R - CNN further improves results , with a margin of 3.0 points box AP over the best previous single model entry from [ reference ] ( which used Inception - ResNet - v2 - TDM ) . Implementation : We apply our Mask R - CNN models with the ResNet - FPN - 50 backbone ; we found the 101 - layer counterpart performs similarly due to the small dataset size . We train with image scale ( shorter side ) randomly sampled from [ 800 , 1024 ] , which reduces overfitting ; inference is on a single scale of 1024 pixels .
In this paper , we propose a novel object detection approach based on the Multiscale-SELU-DenseNet ( MSE-DenseNet ) and the dynamic anchor assignment ( DAA ) strategy . We fuse a highly modified DenseNet and a feature pyramid network into the MSE-DenseNet to extract multi-level feature descriptions of RS images , and then we use them to detect objects on various scales . To further improve the detection performance , we propose a DAA strategy for assigning prior anchor boxes .
It can learn half and reusing half of the preceding feature maps , and has fewer parameters . Semantic aggregation adopts the Feature Pyramid Network ( FPN ) into the original SSD . Adding more semantic information to each scale to detect different size objects .
We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder ( VFE ) of different scales at point-wise level and project into multiple pseudo-image feature maps . We further propose an attentive voxel feature encoding that outperforms plain VFE and a feature fusion pyramid network to aggregate multi-scale information at feature map level . Experiments on the KITTI benchmark show that a single HVNet achieves the best mAP among all existing methods with a real time inference speed of 31Hz .
In other words , we reuse the lost data from the pooling layers and concatenate that data as extra feature maps to contribute to classification and detection . Meanwhile , in the feature pyramid , we concatenate feature maps of the lower layers and feature maps that are deconvolved from upper layers to make explicit relationships between layers and to effectively increase the number of channels . The results show that our enhanced SSD for gastric polyp detection can realize real-time polyp detection with 50 frames per second ( FPS ) and can improve the mean average precision ( mAP ) from 88.5 % to 90.4 % , with only a little loss in time-performance .
Focusing on these challenges , this paper proposes an end-to-end framework called the multiple feature pyramid network ( MFPN ) . In MFPN , we design an effective feature pyramid and a tailored pyramid pooling module , taking advantage of multilevel semantic features of high-resolution remote sensing images . In the optimization stage , a weighted balance loss function is presented to solve the class imbalance problem caused by the sparseness of roads .
We propose a flexible Attribute Localization Module ( ALM ) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels . Moreover , a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance . The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision .
The low-level features have high resolutions but could not represent the features well . The feature pyramid structure contains multi-scale features . To combine the high and low-level features of the pyramid , the algorithm of this paper applied the deconvolution network to the high-level features of the feature pyramid to get the semantic information , dilated convolution network to learn the position information of the low-level features and used convolution for the middle level features to reduce the feature channels , then used convolution to fuse the features .
However , the current designs for feature pyramids are still inefficient to integrate the semantic information over different scales . In this paper , we begin by investigating current feature pyramids solutions , and then reformulate the feature pyramid construction as the feature reconfiguration process . Finally , we propose a novel reconfiguration architecture to combine low-level representations with high-level semantic features in a highly-nonlinear yet efficient way .
The standard pipeline in pedestrian detection is sliding a pedestrian model on an image feature pyramid to detect pedestrians of different scales . In this pipeline , feature pyramid construction is time consuming and becomes the bottleneck for fast detection . Recently , a method called multiresolution filtered channels ( MRFC ) was proposed which only used single scale feature maps to achieve fast detection .
We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation . Specifically , given two input frames , we forward-warp the frames and their feature pyramid representations based on an optical flow estimate using softmax splatting . In doing so , the softmax splatting seamlessly handles cases where multiple source pixels map to the same target location .
This is achieved by : ( a ) predicting category-sensitive semantic maps for the object existing possibility , and ( b ) producing category-agnostic bounding box for each position that potentially contains an object . The scales of target boxes are naturally associated with feature pyramid representations for each input image . Without bells and whistles , FoveaBox achieves state-of-the-art single model performance of 42.1 AP on the standard COCO detection benchmark .
We show how scalable operators can be explicitly constructed using a Gaussian neighbourhood function . We extend this approach to achieve an efficient approximation via a feature map pyramid that implicitly embodies operator scaling . In both cases we provide performance evaluation with respect to edge localisation .
This paper proposes a fast pixel-wise labeling method called scanning convolutional network ( SCN ) for mudflat aquaculture area detection with infrared remote sensing images . SCN improves the traditional fully convolutional network ( FCN ) by replacing convolution layers with scanning convolution modules ( SCM ) and a feature pyramid design , which simultaneously learns large scale sea-land environmental features and mudflat structure details with less computational costs . A set of Landsat-8 satellite images , with three visible bands and three infrared bands , are used to evaluate the proposed method .
Within person search problem , existing pedestrian detectors usually generate person bounding boxes with different sizes , which will degrade the performance of person re-identification in matching process . The built-in feature pyramid hierarchy to address the multi-scale matching problem adopted in previous studies , is prone to be effective for reducing the semantic gaps among different level features . Therefore , in this paper we propose an Adaptive Feature Pyramid (AFP)-based multi-scale matching method for person search .
To achieve a better balance of accuracy and computational complexity for optical flow estimation , a Structural Pyramid Network ( StruPyNet ) is designed to combine structural pyramid processing and feature pyramid processing . In order to efficiently distribute parameters and computations among all structural pyramid levels , the proposed StruPyNet flexibly cascades more small flow estimators at lower structural pyramid levels and less small flow estimators at higher structural pyramid levels .
On the other hand , deep layers obtain more semantic information but the spatial resolution is compromised . Recent work like Feature Pyramid Network ( FPN ) proposes a divide and conquer principle . A U - shape structure is attached to maintain both the high spatial resolution and semantic information .
The Faster R - CNN method further proposes the region proposal networks to generate bounding boxes with various scales . Based on the Faster R - CNN , the FPN approach introduces feature pyramid to extract features with different scales from a single image . The SSD method utilizes feature maps from different stages to process visual information at different scales .
How important are lateral connections ? Table 1 ( e ) shows the ablation results of a top - down feature pyramid without the 1×1 lateral connections . This top - down pyramid has strong semantic features and fine resolutions .
In this work , we present a robust legalization algorithm for standard cell placement that minimizes maximum cell movements fast and effectively based on a novel network-flow approach . The idea is inspired by path augmentation but with important differences . In contrast to the classical path augmentation approaches , we resolve bin overflows by finding several candidate paths that guarantee realizable ( legal ) flow solutions .
More specifically , the contribution of the proposed method mainly consists of three components : Augmented Feature Pyramid networks , Self-adaptive Feature Fusion and a Dependencies Computation ( DC ) Module . A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly . Then , the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions .
Therefore , a more powerful hierarchical representation structure can be obtained by effectively aggregating multi-level contexts . We apply Rev-Dense FPN on SSD framework , which reaches 81.1 % mAP ( mean average precision ) on the PASCAL VOC 2007 dataset and 31.2 AP on the MS COCO dataset . The results show that Rev-Dense FPN is more effective in dealing with diversified object sizes .
We show how the FPN model can be used for formal specification and verification of digital systems . The consistent FPN model is actually a state machine , from which we can obtain a consistent marked Petri net ( MPN ) model . Based on the consistent MPN model , the hardware prototype at register transfer level can be easily induced by using the optimization rules .
Fully convolutional neural network ( FCN ) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels , which boiled down all the redundant calculation , and most recent state-of-the-art methods such as Faster-RCNN , SSD , YOLO and FPN use FCN as their backbone . So here comes one question : Can we find a universal strategy to further accelerate FCN with higher accuracy , so could accelerate all the recent FCN-based methods ?
However , no systematic and comprehensive review has been provided for FPNs as knowledge representation formalisms . Giving this evolving research area , this work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms , knowledge representations and FPN models . In addition , we provide a survey of the applications of FPNs for solving practical problems in variety of fields .
In order to eliminate the fixed-pattern noise ( FPN ) in the output image of time-delay-integration CMOS image sensor ( TDI-CIS ) , a FPN correction method based on gray value compensation is proposed . One hundred images are first captured under uniform illumination .
Then the reliability of the multi-state system was determined . Finally , with respect to the different Type 1 importance measures , according to the state probability , multi-state system , the reliability of the state with fuzzy petri nets ( FPN ) composite importance measures ( CIM ) was estimated and the comparison results table between the CIM and the FPN CIM was produced . From the experiment , multi-state system reliability with composite importance measures of fuzzy petri nets in this paper shows that the variances are consistently low , and the measures can be considered to be robust .
The proposed NUC technique combines the advantages of global- and local-based correction methods , thus can effectively eliminate column FPN without losing original thermal details . The performance of the proposed method is systematically evaluated , and is compared with the state-of-the-art column FPN correction solutions using realistic infrared images .
We show how a simple convolutional neural network ( CNN ) can be trained to accurately and robustly regress 6 degrees of freedom ( 6DoF ) 3D head pose , directly from image intensities . We further explain how this FacePoseNet ( FPN ) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks . We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments .
There is a consistent superiority compared to the architectures searched on ImageNet classification . Our main result architecture achieves better performance than ResNet-101 on COCO with the FPN detector . In addition , we illustrate the architectures searched by DetNAS and find some meaningful patterns .
FPN uses a top - down architecture with lateral connections to build an in - network feature pyramid from a single - scale input . Faster R - CNN with an FPN backbone extracts RoI features from different levels of the feature pyramid according to their scale , but otherwise the rest of the approach is similar to vanilla ResNet . Using a ResNet - FPN backbone for feature extraction with Mask R - CNN gives excellent gains in both accuracy and speed .
Segmentation is a pixel - to - pixel task and we exploit the spatial layout of masks by using an FCN . In Table 2e , we compare multi - layer perceptrons ( MLP ) and FCNs , using a ResNet - 50 - FPN backbone . Using FCNs gives a 2.1 mask AP gain over MLPs .
For this result , even though the full Mask R - CNN model is trained , only the classification and box outputs are used at inference ( the mask output is ignored ) . Mask R - CNN using ResNet - 101 - FPN outperforms the base variants of all previous state - ofthe - art models , including the single - model variant of G - RMI [ reference ] , the winner of the COCO 2016 Detection Challenge . Using ResNeXt - 101 - FPN , Mask R - CNN further improves results , with a margin of 3.0 points box AP over the best previous single model entry from [ reference ] ( which used Inception - ResNet - v2 - TDM ) .
Implementation : We apply our Mask R - CNN models with the ResNet - FPN - 50 backbone ; we found the 101 - layer counterpart performs similarly due to the small dataset size . We train with image scale ( shorter side ) randomly sampled from [ 800 , 1024 ] , which reduces overfitting ; inference is on a single scale of 1024 pixels .
A multiscale object detection approach for remote sensing images based on MSE-DenseNet and the dynamic anchor assignment Feature reusing and semantic aggregation for single stage object detector HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection Real-time gastric polyp detection using convolutional neural networks An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization Research on improved algorithm of object detection based on feature pyramid Deep Feature Pyramid Reconfiguration for Object Detection Delving Deep into Multiscale Pedestrian Detection via Single Scale Feature Maps Softmax Splatting for Video Frame Interpolation FoveaBox: Beyond Anchor-based Object Detector Efficient Laplacian feature map pyramids in a hexagonal framework Mudflat aquaculture labeling for infrared remote sensing images via a scanning convolutional network Improving Person Search by Adaptive Feature Pyramid-based Multi-Scale Matching Structural Pyramid Network for Cascaded Optical Flow Estimation Face Attention Network: An Effective Face Detector for the Occluded Faces Res2Net: A New Multi-scale Backbone Architecture Feature Pyramid Networks for Object Detection A Fast, Robust Network Flow-based Standard-Cell Legalization Method for Minimizing Maximum Movement A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images Reverse Densely Connected Feature Pyramid Network for Object Detection Requirements specification and analysis of digital systems using fuzzy and marked Petri nets Beyond Trade-Off: Accelerate FCN-Based Face Detector with Higher Accuracy Fuzzy Petri nets for knowledge representation and reasoning: A literature review A Fixed-Pattern Noise Correction Method Based on Gray Value Compensation for TDI CMOS Image Sensor Multi-state systems reliability with composite importance measures of fuzzy petri nets Spatially Adaptive Column Fixed-Pattern Noise Correction in Infrared Imaging System Using 1D Horizontal Differential Statistics FacePoseNet: Making a Case for Landmark-Free Face Alignment DetNAS: Neural Architecture Search on Object Detection Mask R-CNN
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AIIPCC ICCV ECCV (5) ICASSP VCIP MMM (1) ArXiv IEEE transactions on pattern analysis and machine intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ISPD ACCV (5) IEEE Transactions on Pattern Analysis and Machine Intelligence
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