Journal of South China University of Technology (Natural Science Edition) ›› 2021, Vol. 49 ›› Issue (7): 94-102.doi: 10.12141/j.issn.1000-565X.200496

Special Issue: 2021年计算机科学与技术

• Computer Science & Technology • Previous Articles     Next Articles

Vehicle Recognition Method Based on Improved CenterNet

HUANG Yuezhen1,2 WANG Naizhou3,4 LIANG Tiancai 3,5 ZHAO Qingli3   

  1. 1． College of Computer Science and Technology，National University of Defense Technology，Changsha 410022，Hunan，China; 2． Guangzhou Radio Group，Guangzhou 510663，Guangdong，China; 3． Shenzhen Xinyi Technology Co． ，Ltd． ，Shenzhen 518067，Guangdong，China; 4． School of Electronic and Information Engineering，South China University of Technology， Guangzhou 510640，Guangdong，China; 5． School of Automation Science and Engineering，South China University of Technology， Guangzhou 510640，Guangdong，China
  • Received:2020-08-20 Revised:2021-05-06 Online:2021-07-25 Published:2021-07-01
  • Contact: 黄跃珍 ( 1973-) ，男，工程师，博士生，主要从事计算机视觉、深度学习研究。 E-mail:huangyz@grg.net.cn
  • About author:黄跃珍 ( 1973-) ，男，工程师，博士生，主要从事计算机视觉、深度学习研究。
  • Supported by:
    Supported by the National Key R＆D Program of China ( 2018YFB0204301)

PDF

204

Abstract: A vehicle recognition algorithm based on improved CenterNet was proposed to solve the problem of low target recognition rate in vehicle recognition system． Firstly，the algorithm used ResNet18 as the basic network to reduce network parameters． Secondly，in view of the problem that the CenterNet model has the demerits of inaccurate positioning of vehicles，it was proposed to use the distance intersection over union loss to replace offset loss and width-high loss; meanwhile，single-scale spatial feature fusion ( SASFF) method and adaptive hierarchical feature fusion ( AHFF) method were employed to fuse several feature maps of the network． The experimental results show that on the Vehicle data set，mean average precision of the improved CenterNet models increases by 1. 9% ; on the BDD100K and Pascal VOC data sets，average precision at 0. 5 of the intersection over union between the predicted boxes and the true boxes increase by 5. 2% and 2. 5% ，respectively，and the inference speed on GTX1080Ti can reach 149 f /s． The improved CenterNet proposed in this paper can significantly improve vehicle recognition accuracy．

Key words: vehicle recognition, target detection, deep learning, feature fusion

CLC Number: