计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 239-246.doi: 10.11896/jsjkx.210200119
徐涛, 陈奕仁, 吕宗磊
XU Tao, CHEN Yi-ren, LYU Zong-lei
摘要： 文中提出了一种基于先验知识和改进YOLOv3算法的机坪工作人员反光背心检测算法。该方法针对现有目标检测方法速度偏低的问题,基于先验知识生成反光背心检测候选区域来替换初始候选区域,以减少检测区域面积,使用Darknet-37替代Darknet-53作为骨干网络进行特征提取,提高了算法的检测速度。针对反光背心在画面中所占面积偏小,且辨识难度较大的问题,在检测模型中加入空间金字塔池化结构(SPP),从而实现特征增强,并将检测尺度提升至4个,以进行多尺度特征融合。使用K-means++算法对标注边界框尺寸重新进行聚类分析,并用聚类结果替换YOLOv3初始Anchor值。选取GIoU作为损失函数以提高定位精准度。实验结果表明,所提出的新型目标检测算法在自建的反光背心数据集上取得了优于YOLOv3的检测结果,精准率和召回率分别达到了97.6%和96.1%,检测速度达到了28.4帧/s,有效解决了原模型中存在的定位不准、漏检、检测速度偏低等问题,在保证检测精度较高的情况下满足了机坪目标检测在实际运用中的实时性要求。
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| [1] ZHAO Y Q,RAO Y,DONG S P,et al.Survey on deep learning object detection[J].Journal of Image and Graphics,2020,25(4):629-654. [2] GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:1440-1448. [3] REN S,HE K,GRISHICK 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. [4] GRICHICK 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. [5] HE K M,GKIOXARI G,DOLLAR P,et al.Mask R-CNN[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(2):386-397. [6] CHANU M M.A deep learning approach for object detectionand instance segmentation using mask RCNN[J].Journal of Advanced Research in Dynamical and Control Systems,2020,12(SP3):95-104. [7] REDMON J,DIVVALA S,GIRSHICK R,et al.You onlylookonce:Unified,real-timeobject detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Re-cognition.2016:779-788. [8] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shotmultibox detector[C]//Proceedings of the 2016 European Conference on Computer Vision.Cham:Springer,2016:21-37. [9] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7263-7271. [10] REDMON J,FARHADI A.YOLOv3:an incremental impro- vement[J].arXiv:1804.02767,2018. [11] XU Z F,JIA R S,SUN H M,et al.Light-YOLOv3:fast method for detecting green mangoes in complex scenes using picking robots[J].Applied Intelligence,2020(6):1-18. [12] PANG L,LIU H,CHEN Y,et al.Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm[J].Sensors(Basel,Switzerland),2020,20(6):44-50. [13] ZHAO L,LI S.Object Detection Algorithm Based on Improved YOLOv3[J].Electronics,2020,9(3):537. [14] DENG Z,YANG R,LAN R,et al.SE-IYOLOv3:An Accurate Small Scale Face Detector for Outdoor Security[J].Mathema-tics,2020,8(1):93. [15] DENG H,CHENG J,LIU T,et al.Research on iron surface crack detection algorithm based on improve YOLOv4 network[C]//Journal of Physics:Conference Series,2nd International Conference on Artificial Intelligence and Computer Science,Hangzhou,Zhejiang,China,2020:25-26. [16] GU Y H,CAO M T,XIU J Y,et al.Violation Detection algorithm based on YOLOv4 Network[J].Journal of Chongqing University of Technology (Natural Science),2021,35(8):114-121. [17] HOWARD A,SANDLER M,CHU G,et al.Searching for mobilenetv3[C]//Proceedings of the IEEE International Confe-rence on Computer Vision.2019:1314-1324. [18] PAN M Y,SONG H H,ZHANG K H,et al.Learning Global Guided Progressive Feature Aggregation Lightweight Network for Salient Object Detection[J].Computer Science,2021,48(6):103-109. [19] HE K M,ZHANG X Y,REN S Q,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. [20] BACHEM O,LUCIC M,HASSANI S H,et al.Approximate k-means++in sublinear time[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence,2016:1459-1467. [21] BACHEM O,LUCIC M,HASSANI H,et al.Fast and provably good seedings for k-means[C]//30th Conference on Neural Information Processing Systems,2016:55-63. [22] 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 Conference on Computer Vision and Pattern Recognition.2019:658-666. [23] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:to-wards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149. |
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