Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 157-164.doi: 10.11896/jsjkx.210600240

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization

WANG Can1,2, LIU Yong-jian1, XIE Qing1,2, MA Yan-chun1   

  1. 1 School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China
    2 Qinghai-WUT Industrial Technology Research Institute of Culture and Technology Integration,Haidong,Qinghai 810600,China
  • Received:2021-06-30 Revised:2021-12-09 Published:2022-08-02
  • About author:WANG Can,born in 1994,postgra-duate.His main research interests include object detection and so on.
    XIE Qing,born in 1986,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include information retrieval and machine learning.
  • Supported by:
    Characteristic National Culture Vector Digital Resource Reuse and Industrial Innovation Project(Thangka and Fresco Vector Digital Annotation and Process Standard Formulation).

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Abstract: Similar to the Anchor Based object detection algorithm,the Anchor Free object detection algorithm based on feature points also encounters the problem of ambiguous samples when dividing positive and negative samples.That is,the training samples are divided either positive or negative according to the specific threshold and the position of feature points,and when the model trains samples whose feature point is near the borderline,it will incur great loss,which will make the model pay too much attention to these ambiguous samples and reduce the performance of the model.In view of this situation,this paper proposes to improve the Anchor Free object detection algorithm based on feature points from the three aspects of soft label,loss function and weight optimization.By making full use of Center Ness,the impact of ambiguous samples on network performance is mitigated and the accuracy of object detection is improved.To prove the effectiveness of the proposed method,the FCOS object detector is employed in the comparative experiments on the classical Pascal VOC and MS COCO datasets,respectively.Finally,the mAP of the detector on Pascal VOC dataset increases to 82.16%(an increase of 1.31%) and the AP50-95 on MS COCO dataset increases to 35.8% (an increase of 1.3%).

Key words: Ambiguous samples, Anchor Free, Center Ness, Object detection, Sample weight optimization

CLC Number: 

  • TP183
[1]CHEN K Q,ZHU Z L,DENG X M,et al.Deep Learning for Multi-Scale Object Detection:A survey[J].Journal of Software,2021,32(4):1201-1227.
[2]LI S P,LI C L,HAN J B,et al.Application of Binocular Vision Single Step Multi-target Detection Method for Robot Grasping[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2021,38(5):68-74.
[3]XUAN D D,WANG J,WANG Z.Salient target detection based on high-level priori semantics [J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2020,32(2):304-312.
[4]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.2019:9626-9635.
[5]DUAN K,BAI S,XIE L,et al.CenterNet:Keypoint Triplets for Object Detection [C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:6569-6578.
[6]CARION N,MASSA F,SYNNAEVE G,et al.End-to-End Object Detection with Transformers [C]//European Conference on Computer Vision.Cham:Springer,2020:213-229.
[7]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 Confe-rence on Computer Vision and Pattern Recognition.2020:9756-9765.
[8]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.2017:2999-3007.
[9]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,2022,44(4):1922-1933.
[10]KONG T,SUN F,LIU H,et al.FoveaBox:Beyound Anchor-Based Object Detection [J].IEEE Transactions on Image Processing,2020,29:7389-7398.
[11]ZHU C,CHEN F,SHEN Z,et al.Soft Anchor-Point Object Detection[C]//European Conference on Computer Vision.Cham:Springer,2020:91-107.
[12]REDMON J,FARHADI A.YOLOv3:An Incremental Improvement [J].arXiv:1804.02767,2018.
[13]BOCHKOVSKIY A,WANG C Y,LIAO H Y M.YOLOv4:Optimal Speed and Accuracy of Object Detection [J].arXiv:2004.10934,2020.
[14]TAN M,PANG R,LE Q V.EfficientDet:Scalable and Efficient Object Detection [C]//Proceedings of the IEEE/CVFConfe-rence on Computer Vision and Pattern Recognition.2020:10778-10787.
[15]CAI Z,VASCONCELOS N.Cascade R-CNN:Delving Into High Quality Object Detection [C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2018:6154-6162.
[16]HE K,GKIOXARI G,DOLLÁR P,et al.Mask R-CNN [C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2980-2988.
[17]PANG J,CHEN K,SHI J,et al.Libra R-CNN:Towards Ba-lanced Learning for Object Detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:821-830.
[18]LAW H,DENG J.CornerNet:Detecting Objects as Paired Keypoints [C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:765-781.
[19]ZHANG Z,HE T,ZHANG H,et al.Bag of Freebies for Trai-ning Object Detection Neural Networks [J].arXiv:1902.04103,2019.
[20]LI H,WU Z,ZHU C,et al.Learning From Noisy Anchors for One-Stage Object Detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10585-10594.
[21]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.2020:12161-12169.
[22]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[23]YU J,JIANG Y,WANG Z,et al.UnitBox:An Advanced Object Detection Network [C]//Proceedings of the 24th ACM International Conference on Multimedia.2016:516-520.
[24]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.
[25]ZHENG Z,WANG P,LIU W,et al.Distance-IoU Loss:Faster and Better Learning for Bounding Box Regression [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:12993-13000.
[26]LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single ShotMultiBox Detector [C]//European Conference on Computer Vision.Cham:Springer,2016:21-37.
[27]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.
[28]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.
[29]TAN M,LE Q.EfficientNet:Rethinking Model Scaling for Convolutional Neural Networks [C]//International Conference on Machine Learning.PMLR,2019:6105-6114.
[30]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.
[31]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.
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