面向深度卷积神经网络的小目标检测算法综述

doi:10.11896/jsjkx.220500260

Abstract

Abstract: Small object detection has long been one of the most challenging problems in computer vision.Since small objects have the characteristics of small coverage area,low resolution,and lack of feature information,their detection effect is not ideal compared to large-sized objects.In recent years,the small object detection algorithm based on deep convolutional neural networks has developed vigorously,and been successfully used in fields such as satellite remote sensing and driverless vehicles.This survey makes a taxnomy,analysis and comparison of existing algorithms.First,the difficulties of small object detection and common detection datasets are introduced.Second,the existing detection algorithms are systematically described from five aspects:backbone network,pyramid structure,anchor design,optimization of object and a bag of species,to provide ideas for further improving the performance of small object detection algorithms.Then,we briefly summarize the existing small object detection algorithms and analyze their performance of the listed algorithm on common dataset.Finally,the application and the future research direction in the field of small object detection has been prospected.

Key words: Deep learning, Small object detection, Multi-scale feature fusion, Anchor-free, Attention mechanism

CLC Number:

TP391

DU Zi-wei, ZHOU Heng, LI Cheng-yang, LI Zhong-bo, XIE Yong-qiang, DONG Yu-chen, QI Jin. Small Object Detection Based on Deep Convolutional Neural Networks:A Review[J].Computer Science, 2022, 49(12): 205-218.

References

[1]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.
[2]SUN F W,LI C Y,XIE Y Q,et al.Review of Deep Learning Applied to Occluded Object Detection[J].Journal of Frontiers of Computer Science and Technology,2022,16(6):1243-1259.
[3]ZHANG S,ZHU X,LEI Z,et al.S3fd:Single shot scale-inva-riant face detector[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:192-201.
[4]EVERINGHAM M,VAN GOOL L,WILLIAMS C K,et al.The pascal visual object classes (voc) challenge[J].International Journal of Computer Vision,2010,88(2):303-338.
[5]LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft coco:Common objects in context[C]//European Conference on Computer Vision.2014:740-755.
[6]YANG S,LUO P,LOY C C,et al.Wider face:A face detection benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:5525-5533.
[7]YU X,GONG Y,JIANG N,et al.Scale match for tiny person detection[C]//Proceedings of the IEEE/CVF Winter Confe-rence on Applications of Computer Vision.2020:1257-1265.
[8]Detection Leaderboard[EB/OL].https://cocodataset.org/#detection-leaderboard.
[9]XIA G S,BAI X,DING J,et al.DOTA:A large-scale dataset for object detection in aerial images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:3974-3983.
[10]CHENG G,HAN J,ZHOU P,et al.Multi-class geospatial object detection and geographic image classification based on collection of part detectors[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,98:119-132.
[11]WANG J,YANG W,GUO H,et al.Tiny object detection in ae-rial images[C]//2020 25th International Conference on Pattern Recognition(ICPR).2021:3791-3798.
[12]CAO Y,HE Z,WANG L,et al.VisDrone-DET2021:The Vision Meets Drone Object detection Challenge Results[C]//Procee-dings of the IEEE/CVF International Conference on Computer Vision.2021:2847-2854.
[13]ZHANG S,BENENSON R,SCHIELE B.Citypersons:A diverse dataset for pedestrian detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:3213-3221.
[14]ZHU Z,LIANG D,ZHANG S,et al.Traffic-sign detection and classification in the wild[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2016:2110-2118.
[15]BEHRENDT K,NOVAK L,BOTROS R.A deep learning approach to traffic lights:Detection,tracking,and classification[C]//2017 IEEE International Conference on Robotics and Automation(ICRA).2017:1370-1377.
[16]CHEN C,LIU M Y,TUZEL O,et al.R-CNN for small object detection[C]//Asian Conference on Computer Vision.2016:214-230.
[17]WANG J J,WEI J,MEI S H,et al.Improved YOLOv3 for Small Object Detection in Remote Sensing Images[J].Computer Engineering and Applications,2021,57(20):133-141.
[18]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[19]XIE S,GIRSHICK R,DOLLÁR P,et al.Aggregated residualtransformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1492-1500.
[20]ZHANG H,WU C,ZHANG Z,et al.Resnest:Split-attention networks[J].arXiv:2004.08955,2020.
[21]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.
[22]REDMON J,FARHADI A.Yolov3:An incrementalimprove-ment[J].arXiv:1804.02767,2018.
[23]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:Optimal speed and accuracy of object detection[J].arXiv:2004.10934,2020.
[24]GAO S H,CHENG M M,ZHAO K,et al.Res2net:A new multi-scale backbone architecture[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,43(2):652-662.
[25]BROCK A,DE S,SMITH S L,et al.High-performance large-scale image recognition without normalization[C]//Interna-tional Conference on Machine Learning.2021:1059-1071.
[26]NEWELL A,YANG K,DENG J.Stacked hourglass networks for human pose estimation[C]//European Conference on Computer Vision.2016:483-499.
[27]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.
[28]LEE Y,HWANG J W,LEE S,et al.An energy and GPU-computation efficient backbone network for real-time object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019:752-760.
[29]TAN M,LE Q.Efficientnet:Rethinking model scaling for con-volutional neural networks[C]//International Conference on Machine Learning.2019:6105-6114.
[30]LI Y,YAO T,PAN Y,et al.Contextual transformer networks for visual recognition[J].arXiv:2107.12292,2021.
[31]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].arXiv:1706.03762,2017.
[32]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.
[33]QIAO S,CHEN L C,YUILLE A L.DetectoRS:Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution[C]//Computer Vision and Pattern Recognition.2020.
[34]ZHAO W Q,KONG Z X,ZHAO Z B,et al.Small target detection based on a combination of feature pyramid and CornerNet[J].CAAI Transactions on Intelligent Systems,2021,16(1):108-116.
[35]YUAN L,LIU Z Y,ZHU M C,et al.Improved YOLOv3 Remote Sensing Target Detection Based on Improved Dense Connection and Distributional Ranking Loss[J].Computer Science,2021,48(9):168-173.
[36]SRIVASTAVA R K,GREFF K,SCHMIDHUBER J.Highway networks[J].arXiv:1505.00387,2015.
[37]WANG C Y,LIAO H Y M,WU Y H,et al.CSPNet:A newbackbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2020:390-391.
[38]KOU Y C,HOU J,ZENG L M,et al.Road small target detection network based on feedback mechanism and hole convolution[J／OL].Computer Engineering,2022:1-10.[2022-11-21].DOI:10.19678/j.issn.1000-3428.0063575.
[39]GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? the kitti vision benchmark suite[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2012:3354-3361.
[40]IANDOLA F N,HAN S,MOSKEWICZ M W,et al.Squee-zeNet:AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J].arXiv:1602.07360,2016.
[41]HOWARD A G,ZHU M,CHEN B,et al.Mobilenets:Efficient convolutional neural networks for mobile vision applications[J].arXiv:1704.04861,2017.
[42]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.
[43]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.
[44]ZHANG X,ZHOU X,LIN M,et al.Shufflenet:An extremelyefficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6848-6856.
[45]MA N,ZHANG X,ZHENG H T,et al.Shufflenet v2:Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:116-131.
[46]LI Y,CHEN Y,DAI X,et al.MicroNet:Towards image recognition with extremely low FLOPs[J].arXiv:2011.12289,2020.
[47]HAN K,WANG Y,TIAN Q,et al.Ghostnet:More featuresfrom cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:1580-1589.
[48]YANG T J,HOWARD A,CHEN B,et al.Netadapt:Platform-aware neural network adaptation for mobile applications[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:285-300.
[49]ZHANG X,LI N,ZHANG R.An improved lightweight network MobileNetv3 Based YOLOv3 for pedestrian detection[C]//2021 IEEE International Conference on Consumer Electronics and Computer Engineering(ICCECE).IEEE,2021:114-118.
[50]SINGH B,DAVIS L S.An analysis of scale invariance in object detection snip[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:3578-3587.
[51]SINGH B,NAJIBI M,DAVIS L S.Sniper:Efficient multi-scale training[J].Advances in Neural Information Processing Systems,2018,31:9310-9320.
[52]LIU Z,GAO G,SUN L,et al.IPG-net:Image pyramid guidance network for small object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2020:1026-1027.
[53]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.
[54]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.
[55]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.
[56]CHEN K,PANG J,WANG J,et al.Hybrid task cascade for instance segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:4974-4983.
[57]JIANG Y,TAN Z,WANG J,et al.GiraffeDet:A Heavy-Neck Paradigm for Object Detection[J].arXiv:2202.04256,2022.
[58]REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks[J].arXiv:1506.01497,2015.
[59]ZHANG S,WANG X,LEI Z,et al.Faceboxes:A CPU real-time and accurate unconstrained face detector[J].Neurocomputing,2019,364:297-309.
[60]YANG S P,LIU H Z,WANG X Q.Small Size Face Detection Based on Feature Map Fusion[J].Computer Science,2020,47(6):126-132.
[61]ZHEN X K,NIU Y,LI J.Research on Remote Sensing Image Target Detection Based on Improved SSD Algori-thm[J].Laser Journal,2022,43(7):106-112.
[62]ZHOU H,YAN F L,CHU N,et al.Approach to Improve Detection Model for Small Object in Complex Scenes[J].Computer Engineering and Applications,2022,58(11):187-192.
[63]CHEN Q,WANG Y,YANG T,et al.You only look one-level feature[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:13039-13048.
[64]LAW H,DENG J.Cornernet:Detecting objects as paired keypoints[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:734-750.
[65]ZHOU X,ZHUO J,KRAHENBUHL P.Bottom-up object detection by grouping extreme and center points[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:850-859.
[66]DUAN K,BAI S,XIE L,et al.Centernet:Keypoint tri-plets for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:6569-6578.
[67]ZHU C,HE Y,SAVVIDES M.Feature selectiveanchor-freemodule for single-shot object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:840-849.
[68]TIAN Z,SHEN C,CHEN H,et al.Fcos:Fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:9627-9636.
[69]KONG T,SUN F,LIU H,et al.Foveabox:Beyound anchor-based object detection[J].IEEE Transactions on Image Proces-sing,2020,29:7389-7398.
[70]ZHANG H,WANG Y,DAYOUB F,et al.Varifocalnet:An iou-aware dense object detector[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:8514-8523.
[71]SAMET N,HICSONMEZ S,AKBAS E.HoughNet:Integrating near and long-range evidence for visual detection[J].arXiv:2104.06773,2021.
[72]ZHANG S,CHI C,YAO Y,et al.Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:9759-9768.
[73]TIAN Z,SHEN C,CHEN H,et al.Fcos:A simple and strong anchor-free object detector[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,44(4):1922-1933.
[74]CAI Z,VASCONCELOS N.Cascade R-CNN:high quality object detection and instance segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,43(5):1483-1498.
[75]LI A,YANG X,ZHANG C.Rethinking Classification and Localization for Cascade R-CNN[C]//British Machine Vision Conference.2019.
[76]REZATOFIGHI H,TSOI N,GWAK J,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.
[77]ZHENG Z,WANG P,LIU W,et al.Distance-IoU loss:Fasterand better learning for bounding box regression[C]//Procee-dings of the AAAI Conference on Artificial Intelligence.2020:12993-13000.
[78]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.
[79]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.
[80]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.
[81]LUO Y T,JIANG P F,DUAN,et al.Small Object DetectionOriented Improved-RetinaNet Model and Its Application[J].Computer Science,2021,48(10):233-238.
[82]LIU G,HAN J,RONG W.Feedback-driven loss function forsmall object detection[J].Image and Vision Computing,2021,111:104197.
[83]WANG J,XU C,YANG W,et al.A Normalized Gaussian Was-serstein Distance for Tiny Object Detection[J].arXiv:2110.13389,2021.
[84]WANG W G,SHEN J B,JIA Y D.Review of Visual Attention Detection[J].Journal of Software,2019,30(2):416-439.
[85]HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[86]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.
[87]WANG Q,WU B,ZHU P,et al.ECA-Net:Efficient Channel Attention for Deep Convolutional Neural Networks[J].arXiv.1910.03151,2020.
[88]ZHU X,LYU S,WANG X,et al.TPH-YOLOv5:ImprovedYOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:2778-2788.
[89]ZHAO P F,XIE L B,PENG L.Deep Small Object Detection Algorithm Integrating Attention Mechanism[J].Journal of Frontiers of Computer Science and Technology,2022,16(4):927-937.
[90]ZHANG F,JIAO L,LI L,et al.Multiresolution attention ex-tractor for small object detection[J].arXiv:2006.05941,2020.
[91]ZENG X,OUYANG W,YAN J,et al.Crafting GBD-Net for Object Detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40:2109-2123.
[92]LI J,WEI Y,LIANG X,et al.Attentive contexts for object detection[J].IEEE Transactions on Multimedia,2016,19(5):944-954.
[93]BELL S,ZITNICK C L,BALA K,et al.Inside-outside net:Detecting objects in context with skip pooling and recurrent neural networks[C]//Proceedings of the IEEE Conference onCompu-ter Vision and Pattern Recognition.2016:2874-2883.
[94]SONG L,YANG J F,SHANG Q Z,et al.Dense Face Network:A Dense Face Detector Based on Global Context and Visual Attention Mechanism[J].Machine Intelligence Research,2022,19(3):247-256.
[95]LIU Y,WANG R,SHAN S,et al.Structure inference net:Object detection using scene-level context and instance-level relationships[C]//Proceedings of the IEEE Conference onCompu-ter Vision and Pattern Recognition.2018:6985-6994.
[96]FU K,LI J,MA L,et al.Intrinsic Relationship Reasoning for Small Object Detection[J].arXiv:2009.00833,2020.
[97]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.
[98]HUI G B.Research on intelligent perception technology of military small target[C]//The 9th China Conference On Command and Control.2021:101-106.
[99]LIU X,HUANG J,YANG T,et al.Improved small object detection for UAV acquisition based on CenterNet[J].Computer Engineering and Applications,2022,58(14):96-104.
[100]HUANG X,GE Z,JIE Z,et al.Nms by representative region:Towards crowded pedestrian detection by proposal pairing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10750-10759.
[101]LI Z,TANG X,HAN J,et al.Pyramidbox++:High perfor-mance detector for finding tiny face[J].arXiv:1904.00386,2019.
[102]GE Y Y,XU Y J,ZHAO S,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13(3):366-372.
[103]DAI J,LI Y,HE K,et al.R-fcn:Object detection via region-based fully convolutional networks[J].arXiv:1605.06409,2016.
[104]TANG Q,CAO G,JO K H.Integrated feature pyramid network with feature aggregation for traffic sign detection[J].IEEE Access,2021,9:117784-117794.
[105]GE Z,LIU S,LI Z,et al.Ota:Optimal transport assignment for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:303-312.
[106]BAI Y,ZHANG Y,DING M,et al.Sod-mtgan:Small object detection via multi-task generative adversarial network[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:206-221.
[107]LI J,LIANG X,WEI Y,et al.Perceptual generative adversarial networks for small object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1222-1230.
[108]NOH J,BAE W,LEE W,et al.Better to follow,follow to bebetter:Towards precise supervision of feature super-resolution for small object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:9725-9734.

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