Review on Improvement of Typical Object Detection Algorithms in Deep Learning

doi:10.3778/j.issn.1002-8331.2110-0070

Abstract

Abstract: Object detection is one of the most challenging tasks in the field of machine vision, and deep learning is the most mainstream implementation method for object detection. In recent years, the rapid development of deep learning theory and technology has made great progress in object detection algorithms based on deep learning. Scholars have started from data processing, network structure, loss function and other aspects, and a series of improved methods are proposed for object detection algorithms. This article reviews the improvement methods of typical object detection algorithms. The commonly used data sets and performance evaluation indicators are summarized, and the characteristics, advantages and application fields of the data sets are compared. It sorts out the latest improvement ideas of typical deep learning-based object detection algorithms, and discusses, summarizes and compares and analyzes data augmentation, anchor selection, network model construction, prediction anchor selection and loss calculation. Combined with the existing problems, the future research direction of typical object detection algorithms based on deep learning is prospected.

Key words: deep learning, object detection, data augmentation, network structure, loss calculation

摘要： 目标检测是机器视觉领域内最具挑战性的任务之一，深度学习则是目标检测最主流的实现方法。近年来，深度学习理论及技术的快速发展，使得基于深度学习的目标检测算法取得了巨大进展，学者从数据处理、网络结构、损失函数等多方面入手，提出了一系列对于目标检测算法的改进方式。针对典型目标检测算法的改进方式进行综述。归纳了常用数据集和性能评价指标，并对数据集的特点、优势及应用领域进行了对比。梳理了典型的基于深度学习的目标检测算法的最新改进思路，从数据增强、先验框选择、网络模型的构建、预测框的选取及损失计算几个方面分别进行论述、总结与对比分析。结合当前存在的问题，展望了基于深度学习的典型目标检测算法的未来研究方向。

关键词: 深度学习, 目标检测, 数据增强, 网络结构, 损失计算

WANG Xinpeng, WANG Xiaoqiang, LIN Hao, LI Leixiao, YANG Yanyan, MENG Chuang, GAO Jing. Review on Improvement of Typical Object Detection Algorithms in Deep Learning[J]. Computer Engineering and Applications, 2022, 58(6): 42-57.

王鑫鹏, 王晓强, 林浩, 李雷孝, 杨艳艳, 孟闯, 高静. 深度学习典型目标检测算法的改进综述[J]. 计算机工程与应用, 2022, 58(6): 42-57.

References

[1] 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.
[2] LIU W，ANGUELOV D，ERHAN D，et al.SSD：single shot multibox detector[C]//European Conference on Computer Vision.Cham：Springer，2016：21-37.
[3] 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.
[4] 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.
[5] 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.
[6] LAROCA R，SEVERO E，ZANLORENSI L A，et al.A robust real-time automatic license plate recognition based on the YOLO detector[C]//2018 International Joint Conference on Neural Networks（IJCNN），2018：1-10.
[7] 赵阳阳，夏亮，江欣国.基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型[J].交通运输工程学报，2020，20（4）：194-204.
ZHAO Y Y，XIA L，JIANG X G.Short-term metro passenger flow prediction based on EMD-LSTM[J].Journal of Traffic and Transportation Engineering，2020，20（4）：194-204.
[8] 周薇娜，孙丽华，徐志京.复杂环境下多尺度行人实时检测方法[J].电子与信息学报，2021，43（7）：2063-2070.
ZHOU W N，SUN L H，XU Z J.A real-time detection method for multi-scale pedestrians in complex environment[J].Journal of Electronics and Information Technology，2021，43（7）：2063-2070.
[9] 邓天民，周臻浩，方芳，等.改进YOLOv3的交通标志检测方法研究[J].计算机工程与应用，2020，56（20）：28-35.
DENG T M，ZHOU Z H，FANG F，et al.Research on improved YOLOv3 traffic sign detection method[J].Computer Engineering and Applications，2020，56（20）：28-35.
[10] 顾振辉，姜文刚.基于Mask R-CNN改进的遥感图像舰船检测[J].计算机工程与应用，2020，56（8）：171-176.
GU Z H，JIANG W G.Improved remote sensing image ship detection based on mask R-CNN[J].Computer Engineering and Applications，2020，56（8）：171-176.
[11] 侯涛，蒋瑜.改进YOLOv4在遥感飞机目标检测中的应用研究[J].计算机工程与应用，2021，57（12）：224-230.
HOU T，JIANG Y.Application research of improved YOLOv4 in remote sensing aircraft target detection[J].Computer Engineering and Applications，2021，57（12）：224-230.
[12] 苏恒强，郑笃强.基于深度学习技术生猪图像目标检测算法的应用研究[J/OL].吉林农业大学学报：1-8[2021-08-06].https：//doi.org/10.13327/j.jjlau.2020.5779.
SU H Q，ZHENG D Q.Application research of pig image object detection based on deep learning technology[J/OL].Journal of Jilin Agricultural University：1-8[2021-08-06].https：//doi.org/10.13327/j.jjlau.2020.5779.
[13] TONG K，WU Y，ZHOU F.Recent advances in small object detection based on deep learning：a review[J].Image and Vision Computing，2020，97：103910.
[14] 许德刚，王露，李凡.深度学习的典型目标检测算法研究综述[J].计算机工程与应用，2021，57（8）：10-25.
XU D G，WANG L，LI F.Review of typical object detection algorithms for deep learning[J].Computer Engineering and Applications，2021，57（8）：10-25.
[15] 南晓虎，丁雷.深度学习的典型目标检测算法综述[J].计算机应用研究，2020，37（S2）：15-21.
NAN X H，DING L.Review of typical target detection algorithms for deep learning[J].Application Research of Computers，2020，37（S2）：15-21.
[16] KRIZHEVSKY A，SUTSKEVER I，HINTON G E.Imagenet classification with deep convolutional neural networks[J].Advances in Neural Information Processing Systems，2012，25：1097-1105.
[17] SERMANET P，EIGEN D，ZHANG X，et al.Overfeat：integrated recognition，localization and detection using convolutional networks[C]//2nd International Conference on Learning Representations，2014.
[18] SZEGEDY C，LIU W，JIA Y，et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：1-9.
[19] SIMONYAN K，ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv：1409.
1556，2014.
[20] 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.
[21] HU J，SHEN L，ALBANIE S，et al.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：7132-7141.
[22] XIAO Y，TIAN Z，YU J，et al.A review of object detection based on deep learning[J].Multimedia Tools and Applications，2020，79（33）：23729-23791.
[23] LECUN Y，BOTTOU L，BENGIO Y，et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE，1998，86（11）：2278-2324.
[24] KRIZHEVSKY A.Learning multiple layers of features from tiny images[D].University of Tront，2009.
[25] EVERINGHAM M，VAN GOOL L，WILLIAMS C K I，et al.The pascal visual object classes（voc） challenge[J].International Journal of Computer Vision，2010，88（2）：303-338.
[26] EVERINGHAM M，ESLAMI S M A，VAN GOOL L，et al.The pascal visual object classes challenge：a retrospective[J].International Journal of Computer Vision，2015，111（1）：98-136.
[27] RUSSAKOVSKY O，DENG J，SU H，et al.Imagenet large scale visual recognition challenge[J].International Journal of Computer Vision，2015，115（3）：211-252.
[28] LIN T Y，MAIRE M，BELONGIE S，et al.Microsoft coco：common objects in context[C]//European Conference on Computer Vision.Cham：Springer，2014：740-755.
[29] KUZNETSOVA A，ROM H，ALLDRIN N，et al.The open images dataset v4[J].International Journal of Computer Vision，2020，128（7）：1956-1981.
[30] ZHOU B，LAPEDRIZA A，KHOSLA A，et al.Places：a 10 million image database for scene recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，40（6）：1452-1464.
[31] XIAO J，EHINGER K A，HAYS J，et al.Sun database：exploringa large collection of scene categories[J].Inter-national Journal of Computer Vision，2016，119（1）：3-22.
[32] WANG S.Research towards yolo-series algorithms：comparison and analysis of object detection models for real-time UAV applications[J].Journal of Physics：Conference Series，2021，1948（1）：012021.
[33] KANG H J.Real-time object detection on 640×480 image with VGG16+SSD[C]//2019 International Conference on Field-Programmable Technology（ICFPT），2019.
[34] ZHONG Z，ZHENG L，KANG G，et al.Random erasing data augmentation[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：13001-13008.
[35] BOCHKOVSKIY A，WANG C Y，LIAO H Y M.Yolov4：optimal speed and accuracy of object detection[J].arXiv：2004.10934，2020.
[36] INOUE H.Data augmentation by pairing samples for images classification[J].arXiv：1801.02929，2018.
[37] ZHANG H，CISSE M，DAUPHIN Y N，et al.Mixup：beyond empirical risk minimization[C]//International Conference on Learning Representations，2018.
[38] DEVRIES T，TAYLOR G W.Improved regularization of convolutional neural networks with cutout[J].arXiv：1708.
04552，2017.
[39] YUN S，HAN D，OH S J，et al.Cutmix：regularization strategy to train strong classifiers with localizable features[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：6023-6032.
[40] GOODFELLOW I，POUGET-ABADIE J，MIRZA M，et al.Generative adversarial nets[J].Advances in neural Information Processing Systems，2014，27.
[41] CUBUK E D，ZOPH B，MANE D，et al.Autoaugment：learning augmentation strategies from data[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：113-123.
[42] HO D，LIANG E，CHEN X，et al.Population based augmentation：efficient learning of augmentation policy schedules[C]//International Conference on Machine Learning，2019：2731-2741.
[43] LIM S，KIM I，KIM T，et al.Fast autoaugment[J].Advances in Neural Information Processing Systems，2019，32：6665-6675.
[44] 王浩，雷印杰，陈浩楠.改进YOLOV3实时交通标志检测算法[J/OL].计算机工程与应用：1-9[2021-08-20].http：//kns.cnki.net/kcms/detail/11.2127.TP.20210330.1540.018.html.
WANG H，LEI Y J，CHEN H N.Real time traffic sign detection algorithm based on improved YOLOV3[J/OL].Computer Engineering and Applications：1-9[2021-08-20].http：//kns.cnki.net/kcms/detail/11.2127.TP.20210330.1540.
018.html.
[45] 谈世磊，别雄波，卢功林，等.基于YOLOv5网络模型的人员口罩佩戴实时检测[J].激光杂志，2021，42（2）：147-150.
TAN S L，BIE X B，LU G L，et al.Real-time detection for mask-wearing of personnel based on YOLOv5 network model[J].Laser Journal，2021，42（2）：147-150.
[46] DAI X，YUAN X，WEI X.Data augmentation for thermal infrared object detection with cascade pyramid generative adversarial network[J].Applied Intelligence，2021，52（6）：1-15.
[47] ZHU D，XIA S，ZHAO J，et al.Diverse sample generation with multi-branch conditional generative adversarial network for remote sensing objects detection[J].Neurocomputing，2020，381：40-51.
[48] 李文婧，徐国伟，孔维刚，等.基于改进YOLOv4的植物叶茎交点目标检测研究[J].计算机工程与应用，2022，58（4）：221-228.
LI W J，XU G W，KONG W G，et al.Research on target detection of plant leaf-stem intersection based on improved YOLOv4[J].Computer Engineering and Applications，2022，58（4）：221-228.
[49] ARTHUR D，VASSILVITSKII S.K-means++：the advantages of careful seeding[R].Stanford，2006.
[50] GONG J，ZHAO J，LI F，et al.Vehicle detection in thermal images with an improved yolov3-tiny[C]//2020 IEEE International Conference on Power，Intelligent Computing and Systems（ICPICS），2020：253-256.
[51] 刘紫燕，袁磊，朱明成，等.融合SPP和改进FPN的YOLOv3交通标志检测[J].计算机工程与应用，2021，57（7）：164-170.
LIU Z Y，YUAN L，ZHU M C，et al.YOLOv3 traffic sign detection based on SPP and improved FPN[J].Computer Engineering and Applications，2021，57（7）：164-170.
[52] 翁玉尚，肖金球，夏禹.改进Mask R-CNN算法的带钢表面缺陷检测[J].计算机工程与应用，2021，57（19）：235-242.
WENG Y S，XIAO J Q，XIA Y.Strip surface defect detection based on improved Mask R-CNN algorithm[J].Computer Engineering and Applications，2021，57（19）：235-242.
[53] BAHMANI B，MOSELEY B，VATTANI A，et al.Scalable k-means++[J].Proceedings of the VLDB Endowment，2012，5（7）：622-633.
[54] 姜文志，李炳臻，顾佼佼，等.基于改进YOLO V3的舰船目标检测算法[J].电光与控制，2021，28（6）：52-56.
JIANG W Z，LI B Z，GU J J，et al.A ship target detection algorithm based on improved YOLO V3[J].Electronics Optics and Control，2021，28（6）：52-56.
[55] 李云红，张轩，李传真，等.融合DBSCAN的改进YOLOv3目标检测算法[J/OL].计算机工程与应用：1-12[2021-09-24].http：//kns.cnki.net/kcms/detail/11.2127.TP.20210327.
1437.002.html.
LI Y H，ZHANG X，LI C Z，et al.Improved YOLOv3 target detection algorithm combined with DBSCAN[J/OL].Computer Engineering and Applications：1-12[2021-09-24].http：//kns.cnki.net/kcms/detail/11.2127.TP.20210327.
1437.002.html.
[56] ZHANG Y，SONG C，ZHANG D.Deep learning-based object detection improvement for tomato disease[J].IEEE Access，2020，8：56607-56614.
[57] HUANG G，LIU Z，VAN DER MAATEN L，et al.Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：4700-4708.
[58] LIN T Y，DOLLáR 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.
[59] LIU S，QI L，QIN H，et al.Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：8759-8768.
[60] GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision，2015：1440-1448.
[61] XIE S，GIRSHICK R，DOLLáR P，et al.Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：1492-1500.
[62] TAN M，LE Q.Efficientnet：rethinking model scaling for convolutionalneural networks[C]//International Conference on Machine Learning，2019：6105-6114.
[63] TAN M，PANG R，LE Q V.Efficientdet：scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：10781-10790.
[64] 伍锡如，凌星雨.基于改进的Faster RCNN面部表情检测算法[J].智能系统学报，2021，16（2）：210-217.
WU X R，LING X Y.Facial expression recognition based on improved Faster RCNN[J].CAAI Transactions on Intelligent Systems，2021，16（2）：210-217.
[65] ZHAI S P，SHANG D R，WANG S H，et al.DF-SSD：an improved SSD object detection algorithm based on dense net and feature fusion[J].IEEE Access，2020，8：24344-24357.
[66] 喻丽春，刘金清.基于改进Mask R-CNN的火焰图像识别算法[J].计算机工程与应用，2020，56（21）：194-198.
YU L C，LIU J Q.Fire image recognition algorithm based on improved mask R-CNN[J].Computer Engineering and Applications，2020，56（21）：194-198.
[67] 陈睿龙，罗磊，蔡志平，等.基于深度学习的实时吸烟检测算法[J].计算机科学与探索，2021，15（2）：327-337.
CHEN R l，LUO L，CAI Z P，et al.Algorithm for real-time smoking detection based on deep learning[J].Journal of Frontiers of Computer Science and Technology，2021，15（2）：327-337.
[68] 顾恭，徐旭东.改进YOLOv3的车辆实时检测与信息识别技术[J].计算机工程与应用，2020，56（22）：173-184.
GU G，XU X D.Real-time vehicle detection and infor-mation recognition technology based on YOLOv3 improved algorithm[J].Computer Engineering and Applications，2020，56（22）：173-184.
[69] 李祥兵，陈炼.基于改进Faster-RCNN的自然场景人脸检测[J].计算机工程，2021，47（1）：210-216.
LI X B，CHEN L.Face Detection in natural scene based on improved Faster-RCNN[J].Computer Engineering，2021，47（1）：210-216.
[70] 罗晖，贾晨，芦春雨，等.基于改进Faster R-CNN的钢轨踏面块状伤损检测方法[J].计算机应用，2021，41（3）：904-910.
LUO H，JIA C，LU C Y，et al.Rail tread block defects detection method based on improved Faster R-CNN[J].Journal of Computer Applications，2021，41（3）：904-910.
[71] 宋艳艳，谭励，马子豪，等.改进YOLOV3算法的视频目标检测[J].计算机科学与探索，2021，15（1）：163-172.
SONG Y Y，TAN L，MA Z H，et al.Video target detection based on improved YOLOV3 algorithm[J].Journal of Frontiers of Computer Science and Technology，2021，15（1）：163-172.
[72] IOFFE S，SZEGEDY C.Batch normalization：accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning，2015：448-456.
[73] SZEGEDY C，VANHOUCKE V，IOFFE S，et al.Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：2818-2826.
[74] SZEGEDY C，IOFFE S，VANHOUCKE V，et al.Inception-v4，inception-resnet and the impact of residual connections on learning[C]//Thirty-first AAAI Conference on Artificial Intelligence，2017.
[75] FARHADI A，REDMON J.Yolov3：an incremental improvement[J].arXiv：1804.02767，2018.
[76] 郭继峰，孙文博，庞志奇，等.一种改进YOLOv4的交通标志识别算法[J/OL].小型微型计算机系统：1-7[2021-10-27].http：//kns.cnki.net/kcms/detail/21.1106.TP.20210623.
1130.004.html.
GUO J F，SUN W B，PANG Z Q，et al.Improved traffic sign recognition algorithm for Yolov4[J/OL].Journal of Chinese Computer Systems：1-7[2021-10-27].http：//kns.cnki.net/kcms/detail/21.1106.TP.20210623.1130.004.html.
[77] SHI P，XU X，NI J，et al.Underwater biological detection algorithm based on improved Faster-RCNN[J].Water，2021，13（17）：2420.
[78] 袁小平，马绪起，刘赛.改进YOLOv3的行人车辆目标检测算法[J].科学技术与工程，2021，21（8）：3192-3198.
YUAN X P，MA X Q，LIU S.An improved algorithm of pedestrian and vehicle detection based on YOLOv3[J].Science Technology and Engineering，2021，21（8）：3192-3198.
[79] LI J，WEI Y，LIANG X，et al.Attentive contexts for object detection[J].IEEE Transactions on Multimedia，2016，19（5）：944-954.
[80] GHIASI G，LIN T Y，LE Q V.Nas-fpn：learning scalable feature pyramid architecture for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：7036-7045.
[81] CAO J，CHEN Q，GUO J，et al.Attention-guided context feature pyramid network for object detection[J].arXiv：2005.11475，2020.
[82] HOWARD A，SANDLER M，CHU G，et al.Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：1314-1324.
[83] HOWARD A G，ZHU M，CHEN B，et al.Mobilenets：efficient convolutional neural networks for mobile vision applications[J].arXiv：1704.04861，2017.
[84] SANDLER M，HOWARD A，ZHU M，et al.Mobilenetv2：inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：4510-4520.
[85] 徐浩，杨德刚，蒋倩倩，等.基于SSD的轻量级车辆检测网络改进[J/OL].计算机工程与应用：1-10[2021-10-02].http：//kns.cnki.net/kcms/detail/11.2127.TP.20210331.0930.002.html.
XU H，YANG D G，JIANG Q Q，et al.Improvement of lightweight vehicle detection network based on SSD[J/OL].Computer Engineering and Applications：1-10[2021-10-02].http：//kns.cnki.net/kcms/detail/11.2127.TP.
20210331.0930.002.html.
[86] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：7263-7271.
[87] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[J].Advances in Neural Information Processing Systems，2015，28：91-99.
[88] BODLA N，SINGH B，CHELLAPPA R，et al.Soft-NMS—improving object detection with one line of code[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：5561-5569.
[89] JIANG B，LUO R，MAO J，et al.Acquisition of localization confidence for accurate object detection[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：784-799.
[90] ZHENG Z，WANG P，LIU W，et al.Distance-IoU loss：fasterand better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：12993-13000.
[91] PAN Y，DONG F.Suppression and enhancement of overlapping bounding boxes scores in object detection[C]//2019 IEEE International Symposium on Signal Processing and Information Technology（ISSPIT），2019：1-4.
[92] HE Y，ZHU C，WANG J，et al.Bounding box regression with uncertainty for accurate object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：2888-2897.
[93] LIU S，HUANG D，WANG Y.Adaptive nms：refining pedestrian detection in a crowd[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：6459-6468.
[94] BOLYA D，ZHOU C，XIAO F，et al.Yolact：real-time instance segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：9157-9166.
[95] ZHENG Z，WANG P，REN D，et al.Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J].arXiv：2005.03572，2020.
[96] WANG X，ZHANG R，KONG T，et al.Solov2：dynamic，faster and stronger[J].arXiv：2003.10152，2020.
[97] 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.
[98] 汪慧兰，戴舒，刘丹，等.交通场景中改进SSD算法的小尺度行人检测研究[J].计算机工程与应用，2022，58（2）：201-207.
WANG H L，DAI S，LIU D，et al.Small-scale pedestrian detection in traffic scenes based on improved SSD algorithm[J].Computer Engineering and Applications，2022，58（2）：201-207.
[99] 罗晖，贾晨，李健.基于改进YOLOv4的公路路面病害检测算法[J].激光与光电子学进展，2021，58（14）：336-344.
LUO H，JIA C，LI J.Road surface disease detection algorithm based on improved YOLOv4[J].Laser and Optoe-lectronics Progress，2021，58（14）：336-344.
[100] CHEN X，LI J.Research on an efficient single-stage multi-object detection algorithm[C]//2019 International Conference on Smart Grid and Electrical Automation（ICSGEA），2019.
[101] BAO W，REN Y，LIANG D，et al.Defect detection algorithm of anti-vibration hammer based on improved cascade R-CNN[C]//2020 International Conference on Intelligent Computing and Human-Computer Interaction（ICHCI），2020.
[102] LV S，LIU K，QIAO Y，et al.Automatic detection method for small size transmission lines defect based on improved YOLOv3[C]//2020 International Conference on Communications，Information System and Computer Engineering（CISCE），2020.
[103] SHRIVASTAVA A，GUPTA A，GIRSHICK R.Training region-based object detectors with online hard example mining[J].IEEE Conference on Computer Vision & Pattern Recognition，2016：761-769.
[104] CHEN K，LI J，LIN W，et al.Towards accurate one-stage object detection with ap-loss[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：5119-5127.
[105] QIAN Q，CHEN L，LI H，et al.DR loss：improving object detection by distributional ranking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：12164-12172.
[106] CUI Y，JIA M，LIN T Y，et al.Class-balanced loss based on effectivenumber of samples[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：9268-9277.
[107] REZATOFIGHI H，TSOI N，GWAK J Y，et al.Generalized intersection over union：a metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：658-666.
[108] WANG F，JIANG M，QIAN C，et al.Residual attention network for image classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：3156-3164.
[109] YUAN X，HUANG H，JIANG Z，et al.An object detection algorithm based on attention mechanism and lightweight network（AMLN）[C]//Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence，2020：64-69.
[110] WOO S，PARK J，LEE J Y，et al.Cbam：convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：3-19.
[111] DAI J，QI H，XIONG Y，et al.Deformable convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：764-773.
[112] ZHU X，HU H，LIN S，et al.Deformable convnets v2：more deformable，better results[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：9308-9316.
[113] 喻清挺，喻维超，喻国平.基于改进R-FCN的交通标志检测[J/OL].计算机工程：1-7[2021-09-11].https：//doi.org/10.19678/j.issn.1000-3428.0060093.
YU Q T，YU W C，YU G P.Traffic sign detection based on improved R-FCN[J/OL].Computer Engineering：1-7[2021-09-11].https：//doi.org/10.19678/j.issn.1000-3428.
0060093.
[114] 王迪聪，白晨帅，邬开俊.基于深度学习的视频目标检测综述[J].计算机科学与探索，2021，15（9）：1563-1577.
WANG D C，BAI C S，WU K J.Survey of video object detection based on deep learning[J].Journal of Frontiers of Computer Science and Technology，2021，15（9）：1563-1577.