Recently solid state drive ( SSD ) based on NAND flash memory chips becomes popular in the consumer electronics market because it is tough on shock and its I/O performance is better than that of conventional hard disk drive . However , as the density of the semiconductor grows higher , the distance between its wires narrows down , their interferences are frequently occurred , and the bit error rate of semiconductor increases . Without consuming additional transmit power and bandwidth , the use of the SSD HM in the cross-layer design system resulted in significant gains when compared to the non-SSD , standard HM system . It is shown that the cross-layer design system with SSD HM has a 10dB gain over the non-SSD HM system in a Nakagami m=1 channel with a frame error rate target of 10−4 . The present invention provides a plurality of input and output devices IO cache operation method , apparatus and system . Said method comprising : a cache management module reads the information block cache , the cache block comprises at least two address information , addressed to at least two of said at least two different SSD cache device ; the cache buffer management module according to the block information in at least two address to the at least two SSD cache device initiates a data read operation . Embodiments of the present invention is arranged in the buffer cache block strip device , multi-device IO request parallel processing to improve performance of the cache block read ; dirty cache block is increased by a mirror copy , improve data reliability . Configuração de hardware servidor de experimentação . Caracterı́stica Descrição Marca/Modelo Dell PowerEdge T640 BCC Processador 2x Intel R Xeon TM Silver 4114 10 cores ( 2.20GHz , 13.75 MB Cache , 9.6GT/s ) Memória 256 GB ( 8x32 GB ) RDIMM DDR4 2667 MT/s Placa de rede 2x 10GbE : Intel I350 ( 10GBase-T ) ; 2x 1GbE : Intel I350 ( 1000Base-T ) Armazenamento 2x SSD SATA 480 GB 6 Gbps ; 3x HDD SATA 4 TB , 7.2 K RPM 3.1 . KVM Para criação do ambiente virtual foi escolhido o hypervisor KVM dada a sua afinidade com plataformas de virtualização abertas e de código livre . This paper investigates two types of graph queries : single source distance ( SSD ) queries and single source shortest path ( SSSP ) queries . Given a node v in a graph G , an SSD query from v asks for the distance from $ v$ to any other node in G , while an SSSP query retrieves the shortest path from v to any other node . These two types of queries find important applications in graph analysis , especially in the computation of graph measures . The embodiment of the invention discloses a slot-type solid state disk ( SSD ) , which is used for fully utilizing the free space of a cabinet . The slot-type SSD in the embodiment of the invention comprises a memory strip circuit board , and an SSD module , an SSD control module and a power supply interface which are fixed on the memory strip circuit board , wherein the bottom of the memory strip circuit board is provided with a golden finger which accords with the specification of a dual in-line memory module ( DIMM ) ; the SSD module is used for storing data ; the SSD control module is used for connecting the SSD module and the golden finger , writing the data transmitted by the golden finger in the SSD module , and transmitting the data to be sent through the golden finger ; and the power supply interface is used for supplying power to the SSD module and the SSD control module . Single-subject designs ( SSD ) appear to be more appropriate for monitoring and statistically evaluating athletic performance than traditional group statistical methods . This paper discusses several SSD options available that produce measures of both statistical and practical significance . Additionally , this paper discusses issues related to heteroscedasticity , reliability , validity and provides recommendations for each . Additionally , their performance characteristics expose gaps in existing benchmarking methodologies . We discuss the impact on Linux system design of a prototype PCI Express SSD that operates at least an order of magnitude faster than most drives available today . We develop benchmarking strategies and focus on several areas where current Linux systems need improvement , and suggest methods of taking full advantage of such high-performance solid state storage . To substantiate our claim , we evaluate three different cluster architectures : 1 ) SupermikeII , a traditional HPC cluster , 2 ) SwatIII , a regular datacenter , and 3 ) CeresII , a MicroBrick-based novel hyperscale system . CeresII with 6-Xeon-D1541 cores ( 2GHz/core ) , 1-NVMe SSD ( 2GBPS I/O-bandwidth ) and 64 GB DRAM per node , closely resembles the optimum produced by our model . Consequently , in terms of price-performance-ratio CeresII outperformed both SupermikeII ( by 65 - 85 % ) and SwatIII ( by 40 - 50 % ) for data-and compute-intensive Hadoop benchmarks ( TeraSort and WordCount ) and our own benchmark genome assembler based on Hadoop and Giraph . For example , Figures I and II show displays of red and led on a Kay Elemetrics Speech Spectrongraphic Display ( SSD 8800 ) . The SSD 8800 is a dedicated , stand-alone piece of hardware which displays patterns of student vocalization . It is transportable , and at West Chester we use it for group demonstrations in the classroom as well as for individual and small-group instruction in the pronunciation lab . In general , there are currently four different types of methods . Region based methods leverage advanced object detection techniques such as Faster RCNN and SSD . Segmentation - based methods mainly utilize fully convolutional neural networks ( FCN ) for generating text score maps , which often need several stages and components to achieve final detections . In this paper , we extend the spirit of fast feature pyramid to CNN and go a few steps further . Our solution to the feature pyramid in CNN descends from the observations of modern CNN - based detectors , including Faster - RCNN , R - FCN , SSD , YOLO and STN , where feature maps are first computed and the detection results are decoded from the maps afterwards . However , the computation cost of generating feature maps becomes a bottleneck for methods using multi - scale testing and it seems not to be a neat solution to the scale variation problem . For 2D RGB image detector , we use the encoder - decoder structure ( e.g. DSSD , FPN ) to generate region proposals from multiple feature maps using focal loss and use Fast R - CNN to predict final 2D detection bounding boxes from the region proposals . To make the detector faster , we take the reduced VGG base network architecture from SSD , sample half of the channels per layer and change all max pooling layers to convolution layers with kernel size and stride of 2 . Then we fine - tune it on ImageNet CLS - LOC dataset for 400k iterations with batch size of 260 on 10 GPUs . 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 . subsubsection : Semantic segmentation . We evaluate the proposed method on both large - scale image classification and challenging fine - grained visual categorization tasks . We make experiments using two PCs each of which is equipped with a 4 - core Intel i7 - 4790k@4.0GHz CPU , 32 G RAM , 512 GB Samsung PRO SSD and two Titan Xp GPUs . We implement our networks using MatConvNet and Matlab2015b , under Ubuntu 14.04.5 LTS . SSD , description = SingleShotMultiboxDetector , first= SSD ( SSD ) CNNname = CNN , As shown in Table [ reference ] , we also compared our method with other state - of - the - art single model . We found that our method outperforms the others with a large margin , including the advanced end - to - end SSD method , which requires complicated data augmentation and careful training skills . Just as discussed earlier , CoupleNet shows a large gain over the classes with occlusions , truncations and considerable background information , like sofa , person , table and chair , which verifies our analyses . The Single Shot Detector ( SSD ) [ reference ] is one of the first attempts at using a ConvNet 's pyramidal feature hierarchy as if it were a featurized image pyramid ( Fig . 1 ( c ) ) . Ideally , the SSD - style pyramid would reuse the multi - scale feature maps from different layers computed in the forward pass and thus come free of cost . But to avoid using low - level features SSD foregoes reusing already computed layers and instead builds the pyramid starting from high up in the network ( e.g. , conv4 3 of VGG nets [ reference ] ) Object detection in densely packed scenes is a new area where standard object detectors fail to train well . Dense object detectors like RetinaNet trained on large and dense datasets show great performance . We train a standard object detector on a small , normally packed dataset with data augmentation techniques . Most of the previous work focuses on classification of fairly large objects in visual imagery . This paper presents a method of detecting and classifying small objects in thermal imagery using a deep learning method based on a RetinaNet network . The result shows that a deep neural network with a relative small set of labelled images can be trained to classify objects in thermal imagery . The discovered architecture , named NAS-FPN , consists of a combination of top-down and bottom-up connections to fuse features across scales . NAS-FPN , combined with various backbone models in the RetinaNet framework , achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models . NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [ 32 ] and achieves 48.3 AP which surpasses Mask R-CNN [ 10 ] detection accuracy with less computation time . We propose a deep learning-based sensor that mitigates the problem of crystal detection in high-density slurries using a segmentation model based on the RetinaNet framework . The sensor functions in ... Some methods utilized the temporal information to boost the detection accuracy , but they are usually expensive in time in estimating optical flow . In this paper , we propose Recurrent RetinaNet , a flexible end-to-end approach for object detection in videos . In this work , a backbone network is leveraged to generate several feature maps , then a feature pyramid network extracts pyramid features from the feature maps . In this paper we focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical . We propose a conceptually simple and efficient joint model of detection and tracking , called RetinaTrack , which modifies the popular single stage RetinaNet approach such that it is amenable to instance-level embedding training . We show , via evaluations on the Waymo Open Dataset , that we outperform a recent state of the art tracking algorithm while requiring significantly less computation . Object detection methods can be divided into two categories that are the two-stage methods with higher accuracy but lower speed and the one-stage methods with lower accuracy but higher speed . In order to inherit the advantages of both approaches , a novel dense object detector , called Path Augmented RetinaNet ( PA-RetinaNet ) , is proposed in this paper . It not only achieves a better accuracy than the two-stage methods , but also maintains the efficiency of the one-stage methods . The main reason for using these types of networks is due to the capability of the networks to automatically localize and detect the license plates in uncontrolled environment . This paper presents the use of one of the deep learning technique which is RetinaNet algorithm and the Convolution Neural Network ( CNN ) ResNet-50 to localize and detect the license plates taken from video images . The videos were taken in real time both during the daytime and night-time . The method was evaluated on over 4500 images acquired by a commercial cervical image acquisition framework . We examined and compared three types of deep learning networks : an object detection model ( RetinaNet ) ; fine-tuned deep learning models ( VGG , Inception ) ; and transfer learning models ( VGG , Inception Feature extractor + SVM ) in evaluating the sharpness of the images . The highest technical image quality assessment accuracy we obtained was 94 % . Nevertheless , our report reveals that for addressing the imbalance to achieve higher accuracy , these schemes are not necessary . Specifically , by three simple training/inference strategies - decoupling objectness from classification , biased initialization , threshold movement , we successfully abandon sampling/reweighting schemes in the representatives of one-stage ( RetinaNet ) , two-stage ( Faster R-CNN ) , and anchor-free ( FCOS ) detectors , with the not worse performance than the vanilla models . As the sampling/reweighting schemes usually introduce laborious hyper-parameters tuning , we expect our discovery could simplify the training procedure of object detectors . The method uses an improved ResNet to extract global features . Local features are extracted using a deep RetinaNet . The global and the local features of the image are merged by an attention mechanism , which are mapped into embedding vectors . subsection : Person Detection Subnet Modern object detectors are classified as one - stage ( SSD , YOLO , RetinaNet ) or two - stage ( Fast R - CNN , Faster R - CNN ) detectors . One - stage detectors enable faster inference but have lower accuracy in comparison to two - stage detectors due to foreground - background class imbalance . The recently proposed RetinaNet model improved one - stage detectors ’ performance with focal loss which can handle the class imbalance problem during training . In order to design a faster and simpler person detection model which is compatible with FPN backbone , we have adopted RetinaNet . We have utilized Tensorflow and Keras deep learning library to implement training and testing procedures . For person detection , we made use of open - source Keras RetinaNet implementation . paragraph : Keypoint Estimation Subnet : One illustrative example for our detection results in the crowd case can be found in Figure [ reference ] . A practical baseline setting is introduced based on the one - shot RetinaNet detector , which obtains comparable performance with fast computation speed . Our FAN which integrates our reproduced one - shot RetinaNet and anchor - level attention significantly outperforms state - of - art detectors on the popular face detection benchmarks including WiderFace and MAFA , especially in the occluded cases like MAFA . section : Related Work IoU - Net by 3.9 % , Cascade R - CNN by 7.6 % and RetinaNet 2 by 7.3 % . This suggests that Cor - 2 We use the best model publicly available on https: // github.com / facebookresearch / Detectron / blob / master / MODEL_ZOO.md nerNet is able to generate bounding boxes of higher quality compared to other state - of - the - art detectors . The main challenges of object detection in VHR remote sensing images are : ( 1 ) VHR images are usually too large and it will consume too much time when locating objects ; ( 2 ) high false alarm because background dominate and is complex in VHR images . To address the above challenges , a new method is proposed to build two-stage object detection model . Our proposed method can be divided into two processes : ( 1 ) we use twice pruning to get region proposal convolutional neural network which is used to predict region proposals ; ( 2 ) and we use once pruning to get classification convolutional neural network which is used to analyze the result of the first stage and output the class labels of proposals . Our results show that Deep Ensemblesoutperform MC Dropout for both types of detectors . We alsointroduce a new merging strategy for sampling-based tech-niques and one-stage object detectors . We show this novelmerging strategy has competitive performance with previ-ously established strategies , while only having one free pa-rameter First , the special pedestrian dataset used for UAV is collected to train the proposed method . Then , we improve YOLOv3 , which is a classical one-stage object detection method , by data sample classification ( normal , far , game scenario ) , adaptive random multi-scale variation , and k-means clustering to make the model robust to scale variation . Compared with the original YOLOv3 pedestrian detection result , the proposed model has a high detection precision ( 90.91 % ) and accuracy ( 80.59 % ) . Our model achieved a 53.3 % average precision score and ran in under three seconds per volume , outperforming the original model and state-of-the-art variants . Results show that the one-stage object detection model is a practical solution , which runs in near real-time and can learn an unbiased feature representation from a large-volume real-world detection dataset , which requires less tedious and time consuming construction of the weak phase-level bounding box labels . Single-stage object detectors have recently gained popularity due to their combined advantage of high detection accuracy and real-time speed . However , while promising results have been achieved by these detectors on standard-sized objects , their performance on small objects is far from satisfactory . Each annotated CCTV image realistically captures the presence of a hand gun under 1 ) varying outdoor and indoor conditions , and 2 ) different resolutions representing variable scales and depth of a gun relative to a cameras sensor . The proposed dataset is used to train a single-stage object detector using a multi-level feature pyramid network ( i.e. M2Det ) . The trained network is then validated using images from the UCF crime video dataset which contains real-world gun violence .
Recently solid state drive ( SSD ) based on NAND flash memory chips becomes popular in the consumer electronics market because it is tough on shock and its I/O performance is better than that of conventional hard disk drive . However , as the density of the semiconductor grows higher , the distance between its wires narrows down , their interferences are frequently occurred , and the bit error rate of semiconductor increases .
Without consuming additional transmit power and bandwidth , the use of the SSD HM in the cross-layer design system resulted in significant gains when compared to the non-SSD , standard HM system . It is shown that the cross-layer design system with SSD HM has a 10dB gain over the non-SSD HM system in a Nakagami m=1 channel with a frame error rate target of 10−4 .
The present invention provides a plurality of input and output devices IO cache operation method , apparatus and system . Said method comprising : a cache management module reads the information block cache , the cache block comprises at least two address information , addressed to at least two of said at least two different SSD cache device ; the cache buffer management module according to the block information in at least two address to the at least two SSD cache device initiates a data read operation . Embodiments of the present invention is arranged in the buffer cache block strip device , multi-device IO request parallel processing to improve performance of the cache block read ; dirty cache block is increased by a mirror copy , improve data reliability .
Configuração de hardware servidor de experimentação . Caracterı́stica Descrição Marca/Modelo Dell PowerEdge T640 BCC Processador 2x Intel R Xeon TM Silver 4114 10 cores ( 2.20GHz , 13.75 MB Cache , 9.6GT/s ) Memória 256 GB ( 8x32 GB ) RDIMM DDR4 2667 MT/s Placa de rede 2x 10GbE : Intel I350 ( 10GBase-T ) ; 2x 1GbE : Intel I350 ( 1000Base-T ) Armazenamento 2x SSD SATA 480 GB 6 Gbps ; 3x HDD SATA 4 TB , 7.2 K RPM 3.1 . KVM Para criação do ambiente virtual foi escolhido o hypervisor KVM dada a sua afinidade com plataformas de virtualização abertas e de código livre .
This paper investigates two types of graph queries : single source distance ( SSD ) queries and single source shortest path ( SSSP ) queries . Given a node v in a graph G , an SSD query from v asks for the distance from $ v$ to any other node in G , while an SSSP query retrieves the shortest path from v to any other node . These two types of queries find important applications in graph analysis , especially in the computation of graph measures .
The embodiment of the invention discloses a slot-type solid state disk ( SSD ) , which is used for fully utilizing the free space of a cabinet . The slot-type SSD in the embodiment of the invention comprises a memory strip circuit board , and an SSD module , an SSD control module and a power supply interface which are fixed on the memory strip circuit board , wherein the bottom of the memory strip circuit board is provided with a golden finger which accords with the specification of a dual in-line memory module ( DIMM ) ; the SSD module is used for storing data ; the SSD control module is used for connecting the SSD module and the golden finger , writing the data transmitted by the golden finger in the SSD module , and transmitting the data to be sent through the golden finger ; and the power supply interface is used for supplying power to the SSD module and the SSD control module .
Single-subject designs ( SSD ) appear to be more appropriate for monitoring and statistically evaluating athletic performance than traditional group statistical methods . This paper discusses several SSD options available that produce measures of both statistical and practical significance . Additionally , this paper discusses issues related to heteroscedasticity , reliability , validity and provides recommendations for each .
Additionally , their performance characteristics expose gaps in existing benchmarking methodologies . We discuss the impact on Linux system design of a prototype PCI Express SSD that operates at least an order of magnitude faster than most drives available today . We develop benchmarking strategies and focus on several areas where current Linux systems need improvement , and suggest methods of taking full advantage of such high-performance solid state storage .
To substantiate our claim , we evaluate three different cluster architectures : 1 ) SupermikeII , a traditional HPC cluster , 2 ) SwatIII , a regular datacenter , and 3 ) CeresII , a MicroBrick-based novel hyperscale system . CeresII with 6-Xeon-D1541 cores ( 2GHz/core ) , 1-NVMe SSD ( 2GBPS I/O-bandwidth ) and 64 GB DRAM per node , closely resembles the optimum produced by our model . Consequently , in terms of price-performance-ratio CeresII outperformed both SupermikeII ( by 65 - 85 % ) and SwatIII ( by 40 - 50 % ) for data-and compute-intensive Hadoop benchmarks ( TeraSort and WordCount ) and our own benchmark genome assembler based on Hadoop and Giraph .
For example , Figures I and II show displays of red and led on a Kay Elemetrics Speech Spectrongraphic Display ( SSD 8800 ) . The SSD 8800 is a dedicated , stand-alone piece of hardware which displays patterns of student vocalization . It is transportable , and at West Chester we use it for group demonstrations in the classroom as well as for individual and small-group instruction in the pronunciation lab .
In general , there are currently four different types of methods . Region based methods leverage advanced object detection techniques such as Faster RCNN and SSD . Segmentation - based methods mainly utilize fully convolutional neural networks ( FCN ) for generating text score maps , which often need several stages and components to achieve final detections .
In this paper , we extend the spirit of fast feature pyramid to CNN and go a few steps further . Our solution to the feature pyramid in CNN descends from the observations of modern CNN - based detectors , including Faster - RCNN , R - FCN , SSD , YOLO and STN , where feature maps are first computed and the detection results are decoded from the maps afterwards . However , the computation cost of generating feature maps becomes a bottleneck for methods using multi - scale testing and it seems not to be a neat solution to the scale variation problem .
For 2D RGB image detector , we use the encoder - decoder structure ( e.g. DSSD , FPN ) to generate region proposals from multiple feature maps using focal loss and use Fast R - CNN to predict final 2D detection bounding boxes from the region proposals . To make the detector faster , we take the reduced VGG base network architecture from SSD , sample half of the channels per layer and change all max pooling layers to convolution layers with kernel size and stride of 2 . Then we fine - tune it on ImageNet CLS - LOC dataset for 400k iterations with batch size of 260 on 10 GPUs .
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 . subsubsection : Semantic segmentation .
We evaluate the proposed method on both large - scale image classification and challenging fine - grained visual categorization tasks . We make experiments using two PCs each of which is equipped with a 4 - core Intel i7 - 4790k@4.0GHz CPU , 32 G RAM , 512 GB Samsung PRO SSD and two Titan Xp GPUs . We implement our networks using MatConvNet and Matlab2015b , under Ubuntu 14.04.5 LTS .
SSD , description = SingleShotMultiboxDetector , first= SSD ( SSD ) CNNname = CNN ,
As shown in Table [ reference ] , we also compared our method with other state - of - the - art single model . We found that our method outperforms the others with a large margin , including the advanced end - to - end SSD method , which requires complicated data augmentation and careful training skills . Just as discussed earlier , CoupleNet shows a large gain over the classes with occlusions , truncations and considerable background information , like sofa , person , table and chair , which verifies our analyses .
The Single Shot Detector ( SSD ) [ reference ] is one of the first attempts at using a ConvNet 's pyramidal feature hierarchy as if it were a featurized image pyramid ( Fig . 1 ( c ) ) . Ideally , the SSD - style pyramid would reuse the multi - scale feature maps from different layers computed in the forward pass and thus come free of cost . But to avoid using low - level features SSD foregoes reusing already computed layers and instead builds the pyramid starting from high up in the network ( e.g. , conv4 3 of VGG nets [ reference ] )
Object detection in densely packed scenes is a new area where standard object detectors fail to train well . Dense object detectors like RetinaNet trained on large and dense datasets show great performance . We train a standard object detector on a small , normally packed dataset with data augmentation techniques .
Most of the previous work focuses on classification of fairly large objects in visual imagery . This paper presents a method of detecting and classifying small objects in thermal imagery using a deep learning method based on a RetinaNet network . The result shows that a deep neural network with a relative small set of labelled images can be trained to classify objects in thermal imagery .
The discovered architecture , named NAS-FPN , consists of a combination of top-down and bottom-up connections to fuse features across scales . NAS-FPN , combined with various backbone models in the RetinaNet framework , achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models . NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [ 32 ] and achieves 48.3 AP which surpasses Mask R-CNN [ 10 ] detection accuracy with less computation time .
We propose a deep learning-based sensor that mitigates the problem of crystal detection in high-density slurries using a segmentation model based on the RetinaNet framework . The sensor functions in ...
Some methods utilized the temporal information to boost the detection accuracy , but they are usually expensive in time in estimating optical flow . In this paper , we propose Recurrent RetinaNet , a flexible end-to-end approach for object detection in videos . In this work , a backbone network is leveraged to generate several feature maps , then a feature pyramid network extracts pyramid features from the feature maps .
In this paper we focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical . We propose a conceptually simple and efficient joint model of detection and tracking , called RetinaTrack , which modifies the popular single stage RetinaNet approach such that it is amenable to instance-level embedding training . We show , via evaluations on the Waymo Open Dataset , that we outperform a recent state of the art tracking algorithm while requiring significantly less computation .
Object detection methods can be divided into two categories that are the two-stage methods with higher accuracy but lower speed and the one-stage methods with lower accuracy but higher speed . In order to inherit the advantages of both approaches , a novel dense object detector , called Path Augmented RetinaNet ( PA-RetinaNet ) , is proposed in this paper . It not only achieves a better accuracy than the two-stage methods , but also maintains the efficiency of the one-stage methods .
The main reason for using these types of networks is due to the capability of the networks to automatically localize and detect the license plates in uncontrolled environment . This paper presents the use of one of the deep learning technique which is RetinaNet algorithm and the Convolution Neural Network ( CNN ) ResNet-50 to localize and detect the license plates taken from video images . The videos were taken in real time both during the daytime and night-time .
The method was evaluated on over 4500 images acquired by a commercial cervical image acquisition framework . We examined and compared three types of deep learning networks : an object detection model ( RetinaNet ) ; fine-tuned deep learning models ( VGG , Inception ) ; and transfer learning models ( VGG , Inception Feature extractor + SVM ) in evaluating the sharpness of the images . The highest technical image quality assessment accuracy we obtained was 94 % .
Nevertheless , our report reveals that for addressing the imbalance to achieve higher accuracy , these schemes are not necessary . Specifically , by three simple training/inference strategies - decoupling objectness from classification , biased initialization , threshold movement , we successfully abandon sampling/reweighting schemes in the representatives of one-stage ( RetinaNet ) , two-stage ( Faster R-CNN ) , and anchor-free ( FCOS ) detectors , with the not worse performance than the vanilla models . As the sampling/reweighting schemes usually introduce laborious hyper-parameters tuning , we expect our discovery could simplify the training procedure of object detectors .
The method uses an improved ResNet to extract global features . Local features are extracted using a deep RetinaNet . The global and the local features of the image are merged by an attention mechanism , which are mapped into embedding vectors .
subsection : Person Detection Subnet Modern object detectors are classified as one - stage ( SSD , YOLO , RetinaNet ) or two - stage ( Fast R - CNN , Faster R - CNN ) detectors . One - stage detectors enable faster inference but have lower accuracy in comparison to two - stage detectors due to foreground - background class imbalance . The recently proposed RetinaNet model improved one - stage detectors ’ performance with focal loss which can handle the class imbalance problem during training . In order to design a faster and simpler person detection model which is compatible with FPN backbone , we have adopted RetinaNet .
We have utilized Tensorflow and Keras deep learning library to implement training and testing procedures . For person detection , we made use of open - source Keras RetinaNet implementation . paragraph : Keypoint Estimation Subnet :
One illustrative example for our detection results in the crowd case can be found in Figure [ reference ] . A practical baseline setting is introduced based on the one - shot RetinaNet detector , which obtains comparable performance with fast computation speed . Our FAN which integrates our reproduced one - shot RetinaNet and anchor - level attention significantly outperforms state - of - art detectors on the popular face detection benchmarks including WiderFace and MAFA , especially in the occluded cases like MAFA . section : Related Work
IoU - Net by 3.9 % , Cascade R - CNN by 7.6 % and RetinaNet 2 by 7.3 % . This suggests that Cor - 2 We use the best model publicly available on https: // github.com / facebookresearch / Detectron / blob / master / MODEL_ZOO.md nerNet is able to generate bounding boxes of higher quality compared to other state - of - the - art detectors .
The main challenges of object detection in VHR remote sensing images are : ( 1 ) VHR images are usually too large and it will consume too much time when locating objects ; ( 2 ) high false alarm because background dominate and is complex in VHR images . To address the above challenges , a new method is proposed to build two-stage object detection model . Our proposed method can be divided into two processes : ( 1 ) we use twice pruning to get region proposal convolutional neural network which is used to predict region proposals ; ( 2 ) and we use once pruning to get classification convolutional neural network which is used to analyze the result of the first stage and output the class labels of proposals .
Our results show that Deep Ensemblesoutperform MC Dropout for both types of detectors . We alsointroduce a new merging strategy for sampling-based tech-niques and one-stage object detectors . We show this novelmerging strategy has competitive performance with previ-ously established strategies , while only having one free pa-rameter
First , the special pedestrian dataset used for UAV is collected to train the proposed method . Then , we improve YOLOv3 , which is a classical one-stage object detection method , by data sample classification ( normal , far , game scenario ) , adaptive random multi-scale variation , and k-means clustering to make the model robust to scale variation . Compared with the original YOLOv3 pedestrian detection result , the proposed model has a high detection precision ( 90.91 % ) and accuracy ( 80.59 % ) .
Our model achieved a 53.3 % average precision score and ran in under three seconds per volume , outperforming the original model and state-of-the-art variants . Results show that the one-stage object detection model is a practical solution , which runs in near real-time and can learn an unbiased feature representation from a large-volume real-world detection dataset , which requires less tedious and time consuming construction of the weak phase-level bounding box labels .
Single-stage object detectors have recently gained popularity due to their combined advantage of high detection accuracy and real-time speed . However , while promising results have been achieved by these detectors on standard-sized objects , their performance on small objects is far from satisfactory .
Each annotated CCTV image realistically captures the presence of a hand gun under 1 ) varying outdoor and indoor conditions , and 2 ) different resolutions representing variable scales and depth of a gun relative to a cameras sensor . The proposed dataset is used to train a single-stage object detector using a multi-level feature pyramid network ( i.e. M2Det ) . The trained network is then validated using images from the UCF crime video dataset which contains real-world gun violence .
Reliability and performance enhancement technique for SSD array storage system using RAID mechanism A Framework for the Transmission of Multimedia Traffic using HM and RS Coding over Nakagami-M Channels Disk cache method, device, and systemproviding Multi-device mirror images and stripe function NovaGenesis NameBindings: Avaliação de Virtualização em Escala Efficient single-source shortest path and distance queries on large graphs Slot-type solid state disk (SSD) and data transmission method Longitudinal Monitoring of Athletes: Statistical Issues and Best Practices High performance solid state storage under Linux Augmenting Amdahl's Second Law: A Theoretical Model to Build Cost-Effective Balanced HPC Infrastructure for Data-Driven Science Computer Graphics in the Language Lab Fused Text Segmentation Networks for Multi-oriented Scene Text Detection Recurrent Scale Approximation for Object Detection in CNN Frustum PointNets for 3D Object Detection from RGB-D Data Res2Net: A New Multi-scale Backbone Architecture Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization Implicit 3D Orientation Learning for 6D Object Detection from RGB Images CoupleNet: Coupling Global Structure with Local Parts for Object Detection Feature Pyramid Networks for Object Detection Benchmark for Generic Product Detection: A strong baseline for Dense Object Detection Detecting and classifying small objects in thermal imagery using a deep neural network NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection A Deep Learning Image-Based Sensor for Real-Time Crystal Size Distribution Characterization Recurrent RetinaNet: A Video Object Detection Model Based on Focal Loss RetinaTrack: Online Single Stage Joint Detection and Tracking PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection Object Localization and Detection for Real-Time Automatic License Plate Detection (ALPR) System Using RetinaNet Algorithm Deep Learning for Assessing Image Focus for Automated Cervical Cancer Screening Revisiting Foreground-Background Imbalance in Object Detectors Research on Image Caption Method Based on Mixed Image Features MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Face Attention Network: An Effective Face Detector for the Occluded Faces CornerNet: Detecting Objects as Paired Keypoints Two-Stage Object Detection Based on Deep Pruning for Remote Sensing Image Benchmarking Sampling-based Probabilistic Object Detectors Pedestrian Detection Using Optimized YOLOv3 in UAV Scenario Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector Efficient Featurized Image Pyramid Network for Single Shot Detector Gun Detection in Surveillance Videos using Deep Neural Networks
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KDD MSST CLOUD 2018 24th International Conference on Pattern Recognition (ICPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IEEE transactions on pattern analysis and machine intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ECCV 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICONIP (4) ICANN (2) IntelliSys (1) BHI ECCV ArXiv ECCV KSEM (1) CVPR Workshops CVPR APSIPA
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