Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210600142-7.doi: 10.11896/jsjkx.210600142

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Multi-label Vehicle Real-time Recognition Algorithm Based on YOLOv3 and Improved VGGNet

GU Xi-long, GONG Ning-sheng, HU Qian-sheng   

  1. College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:GU Xi-long,born in 1993,postgraduate.His main research interests include deep learning and target detection.
    GONG Ning-sheng,born in 1958,Ph.D,professor.His main research interests include mathematical logic,BP neural network,image processing,pattern reco-gnition,data mining and so on.
  • Supported by:
    National Key Basic Research and Development Program(973 Program)(2005CB321901),Mobile Environment Law Enforcement Video Acquisition and Man-agement System Based on High Compression Ratio Technology(ZX16487470001) and Open Project of the State Key Laboratory of Software Development Environment(BUAA-SKLSDE-09KF-03).

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Abstract: In order to quickly and effectively identify vehicle information in video,this paper combines the advantages of YOLOv3 algorithm and CNN algorithm to design an algorithm that can identify vehicle multi-label information in real time.Firstly,the high recognition speed and accuracy of YOLOv3 are used to realize real-time monitoring and positioning of vehicles in video stream.After obtaining the vehicle location information,the vehicle information is passed into the improved simplified and optimized VGGNet multi-label classification network to identify the vehicle with multiple tags.Finally,the label information is output to the video stream to obtain real-time multi-label recognition of vehicles in video.The training and test data sets in this paper are derived from KITTI data sets and multi-label data sets obtained through Bing Image Search API.Experimental results show that the mAP of the proposed method on KITTI data set reaches 91.27,the average accuracy of multi-label is more than 80%,and the frame rate of video reaches 35fps.It achieves good results in vehicle identification and multi-label classification on the basis of ensuring real-time performance.

Key words: Computer vision, Vehicle recognition, Multi-label recognition, Target detection, Deep learning

CLC Number: 

  • TP183
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