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.

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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
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