基于深度学习的目标检测算法研究进展
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摘要
目标检测是计算机视觉领域的核心任务之一。随着深度学习的迅猛发展,基于深度学习的目标检测技术已经成为该领域的主流算法,被广泛应用于人脸检测、车辆检测、行人检测以及无人驾驶等领域。本文系统总结了当前基于深度学习的目标检测算法的研究进展,对各算法的优、缺点及其在VOC2007和COCO数据集上的检测结果进行了全面分析,并对基于深度学习的目标检测算法的未来发展进行了展望。
Object detection is one of the core tasks in the field of computer vision. In recent years, with the rapid development of deep learning, the object detection technology based on deep learning has become the very popular mainstream algorithm. It has been widely used in many fields, such as face detection, vehicle detection, pedestrian detection, and unmanned driving, etc.. This paper systematically summarizes the current research progress of deep learning-based object detection algorithms, and thoroughly analyzes the advantages and disadvantages of each algorithm and its results on the datasets VOC2007 and COCO. Finally, the future development of object detection based on deep learning is also discussed in this paper.
Object detection is one of the core tasks in the field of computer vision. In recent years, with the rapid development of deep learning, the object detection technology based on deep learning has become the very popular mainstream algorithm. It has been widely used in many fields, such as face detection, vehicle detection, pedestrian detection, and unmanned driving, etc.. This paper systematically summarizes the current research progress of deep learning-based object detection algorithms, and thoroughly analyzes the advantages and disadvantages of each algorithm and its results on the datasets VOC2007 and COCO. Finally, the future development of object detection based on deep learning is also discussed in this paper.
引文
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[14] GIRSHICK R.FAST R-CNN[C]//Fast R-CNN.Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448.
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[18] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788.
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[21] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//European Conference on Computer Vision.Springer,2016:21-37.
[22] LIN T Y,DOLL R P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125.
[23] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2980-2988.
[24] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[25] SHEN Z,LIU Z,LI J,et al.dsod:learning deeply supervised object detectors from scratch[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:1919-1927.
[26] JEONG J,PARK H,KWAK N.Enhancement of SSD by concatenating feature maps for object detection[EB/OL].[2019-05-10].https://arxiv.org/pdf/1705.09587.pdf.
[2] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[3] DALAL N,TRIGGS B.Histograms of oriented gradients for human detection[C]//International Conference on Computer Vision Pattern Recognition (CVPR′05).San Diego,2005:886-893.
[4] FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object detection with discriminatively trained part-based models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,32(9):1627-1645.
[5] CRISTIANINI N,SHAWE-TAYLOR J.An introduction to support vector machines and other kernel-based learning methods[M].Cambridge:Cambridge University Press,2000.
[6] 许必宵,宫婧,孙知信.基于卷积神经网络的目标检测模型综述[J].计算机技术与发展,2019(11):1-8.
[7] 张姗,逯瑜娇,罗大为.基于深度学习的目标检测算法综述[C]//中国计算机用户协会网络应用分会2018年第二十二届网络新技术与应用年会,中国计算机用户协会网络应用分会.苏州,2018.
[8] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks[C]//Advances in neural Information Processing Systems.2012:1097-1105.
[9] 李丹.基于深度学习的目标检测综述[J].科技经济导刊,2019,27(13):1-2,31.
[10] 刘晓楠,王正平,贺云涛,等.基于深度学习的小目标检测研究综述[J].战术导弹技术,2019(1):100-107.
[11] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587.
[12] UIJLINGS J R,VAN DE SANDE K E,GEVERS T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[13] HE K,ZHANG X,REN S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[14] GIRSHICK R.FAST R-CNN[C]//Fast R-CNN.Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448.
[15] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems.2015:91-99.
[16] DAI J,LI Y,HE K,et al.R-FCN:Object detection via region-based fully convolutional networks[C]// Advances in Neural Information Processing Systems.2016:379-387.
[17] HE K,GKIOXARI G,DOLL R P,et al.Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2961-2969.
[18] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788.
[19] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7263-7271.
[20] REDMON J,FARHADI A.Yolov3:an incremental improvement[EB/OL].[2019-04-25].https://arxiv.org/PDF/1804.02767.pdf.
[21] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//European Conference on Computer Vision.Springer,2016:21-37.
[22] LIN T Y,DOLL R P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125.
[23] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2980-2988.
[24] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[25] SHEN Z,LIU Z,LI J,et al.dsod:learning deeply supervised object detectors from scratch[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:1919-1927.
[26] JEONG J,PARK H,KWAK N.Enhancement of SSD by concatenating feature maps for object detection[EB/OL].[2019-05-10].https://arxiv.org/pdf/1705.09587.pdf.