Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (8): 106-110.doi: 10.11896/jsjkx.200700161

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Lightweight Anchor-free Object Detection Algorithm Based on Keypoint Detection

GONG Hao-tian, ZHANG Meng   

  1. National ASIC Engineering Center,Southeast University,Nanjing 210096,China
  • Received:2020-07-26 Revised:2020-12-01 Published:2021-08-10
  • About author:GONG Hao-tian,born in 1996,postgraduate.His main research interests include deep learning and computer vision.(220184705@seu.edu.cn)ZHANG Meng,born in 1964,Ph.D,associate professor,Ph.D supervisor.His main research interests include deep learning,machine learning,digital signal processing,digital communication systems,wireless sensor networks,digital integrated circuit design,information security and assurance,etc.

PDF (PC)

1044

Abstract: According to the large number of parameters of key-point object detection network and the problem of mismatching of bounding box,this paper proposes a lightweight key point anchor-free object detection algorithm.It inputs the image into the improved hourglass network to extract features,through the cascade corner pooling module and center pooling module,outputs three key points heatmap and their embedding vectors.At last,it matchs the key points by embedding vectors and draw the bounding box.The innovation of this paper is to applying the firemodule of SqueezeNet in the CenterNet object detection network,and replace the conventional convolution in the backbone with the depth separable convolution.At the same time,aiming at the mismatching bounding box problem in CenterNet,this algorithm adjusts the network's output and loss function.Experiment results show that the model size is reduced to 1/7 of CenterNet,while the accuracy and inference speed are still higher than the same size target detection algorithm like YOLOv3 and CornerNet-Lite.

Key words: Anchor-free, Convolution network, Key point, Lightweight, Object detection

CLC Number: 

  • TP391
[1]REDMON J,FARHADI A.Yolov3:An incremental improve-ment[J].arXiv:1804.02767.
[2]REDMON J,FARHADI A.YOLO9000:Better,Faster,Stronger[C]//IEEE Conference on Computer Vision & Pattern Recognition.2017:6517-6525.
[3]LAW H,DENG J.Cornernet:Detecting objects as paired key-points[C]//Proceedings of the European Conference on Computer Vision.2018:734-750.
[4]DUAN K W,BAI S,XIE L X.CenterNet:Keypoint Triplets for Object Detection[C]//Proceedings of the European Conference on Computer Vision.2019.
[5]REN S Q,HE K M,GIRSHICK R.Faster R-CNN:TowardsReal-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[6]GIRSHICK R.Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV).2015:1440-1448.
[7]HE K,GKIOXARI G,DOLLAR P,et al.Mask r-cnn[C]//Pro-ceedings of the IEEE International Conference on Computer Vision.2017:2961-2969.
[8]ZHOU X,WEI G,FU W L,et al.Application of deep learning in object detection[C]//2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).IEEE,2017.
[9]MURPHY K P.Object detection and localization using local and global features[J].Toward Category Level Object Recognition,2006,12(1):382-400.
[10]WANG W,SHEN J,SHAO L.Video Salient Object Detectionvia Fully Convolutional Networks[J].IEEE Transactions on Image Processing,2017,27(1):38-49.
[11]YANG J,LIU Q S,ZHANG K H.Stacked Hourglass Network for Robust Facial Landmark Localisation[C]//IEEE Conference on Computer Vision & Pattern Recognition Workshops.IEEE Computer Society,2017.
[12]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.
[13]XIAO B,WU H,WEI Y.Simple baselines for human pose estimation and tracking[C]//ECCV.2018:472-478.
[14]SANDLER M,HOWARD A,ZHU M L,et al.MobileNetV2:Inverted Residuals and Linear Bottlenecks [C]//IEEEConfe-rence on Computer Vision and Pattern Recognition (CVPR).2018:4510-4520.
[15]IANDOLA F N,HAN S,MOSKEWICZ M W,et al.Squeeze-Net:AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size[J].arXiv,1602.07360.
[16]SZEGEDY C,LIU W,JIA Y,et al.Going Deeper with Convolutions[J].arXiv:1409.4842.
[17]HUANG G,LIU Z,LAURENS V D M,et al.Densely Connec-ted Convolutional Networks[J].arXiv:1608.06993,2016.
[18]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//CVPR.2016.
[19]HE K,ZHANG X,REN S,et al.Identity Mappings in Deep Residual Networks[C]//European Conference on Computer Vision.Cham:Springer,2016.
[20]LAW H,TENG Y,RUSSAKOVSKY O,et al.CornerNet-Lite:Efficient Keypoint Based Object Detection[J].arXiv:1904.08900.
[1] LIU Dong-mei, XU Yang, WU Ze-bin, LIU Qian, SONG Bin, WEI Zhi-hui. Incremental Object Detection Method Based on Border Distance Measurement [J]. Computer Science, 2022, 49(8): 136-142.
[2] WANG Can, LIU Yong-jian, XIE Qing, MA Yan-chun. Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization [J]. Computer Science, 2022, 49(8): 157-164.
[3] HAO Qiang, LI Jie, ZHANG Man, WANG Lu. Spatial Non-cooperative Target Components Recognition Algorithm Based on Improved YOLOv3 [J]. Computer Science, 2022, 49(6A): 358-362.
[4] LI Jian-zhi, WANG Hong-ling, WANG Zhong-qing. Automatic Generation of Patent Summarization Based on Graph Convolution Network [J]. Computer Science, 2022, 49(6A): 172-177.
[5] CHEN Yong-ping, ZHU Jian-qing, XIE Yi, WU Han-xiao, ZENG Huan-qiang. Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss [J]. Computer Science, 2022, 49(6A): 424-428.
[6] CHEN Jia-zhou, ZHAO Yi-bo, XU Yang-hui, MA Ji, JIN Ling-feng, QIN Xu-jia. Small Object Detection in 3D Urban Scenes [J]. Computer Science, 2022, 49(6): 238-244.
[7] HU Fu-yuan, WAN Xin-jun, SHEN Ming-fei, XU Jiang-lang, YAO Rui, TAO Zhong-ben. Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 10-24.
[8] XU Tao, CHEN Yi-ren, LYU Zong-lei. Study on Reflective Vest Detection for Apron Workers Based on Improved YOLOv3 Algorithm [J]. Computer Science, 2022, 49(4): 239-246.
[9] ZHAO Yue, YU Zhi-bin, LI Yong-chun. Cross-attention Guided Siamese Network Object Tracking Algorithm [J]. Computer Science, 2022, 49(3): 163-169.
[10] XIE Yu, YANG Rui-ling, LIU Gong-xu, LI De-yu, WANG Wen-jian. Human Skeleton Action Recognition Algorithm Based on Dynamic Topological Graph [J]. Computer Science, 2022, 49(2): 62-68.
[11] YUAN Lei, LIU Zi-yan, ZHU Ming-cheng, MA Shan-shan, CHEN Lin-zhou-ting. Improved YOLOv3 Remote Sensing Target Detection Based on Improved Dense Connection and Distributional Ranking Loss [J]. Computer Science, 2021, 48(9): 168-173.
[12] LI Lin, LIU Xue-liang, ZHAO Ye, JI Ping. Low Light Image Fusion Detection Method Based on Lego Filter and SSD [J]. Computer Science, 2021, 48(7): 213-218.
[13] XIN Yuan-xue, SHI Peng-fei, XUE Rui-yang. Moving Object Detection Based on Region Extraction and Improved LBP Features [J]. Computer Science, 2021, 48(7): 233-237.
[14] CHENG Si-wei, GE Wei-yi, WANG Yu, XU Jian. BGCN:Trigger Detection Based on BERT and Graph Convolution Network [J]. Computer Science, 2021, 48(7): 292-298.
[15] ZHANG Man, LI Jie, ZHU Xin-zhong, SHEN Ji, CHENG Hao-tian. Augmentation Technology of Remote Sensing Dataset Based on Improved DCGAN Algorithm [J]. Computer Science, 2021, 48(6A): 80-84.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.