Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 179-184.doi: 10.11896/jsjkx.220300251

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Traffic Sign Detection and Recognition Method Based on Optimized YOLO-V4

PAN Hui-ping1,2, WANG Min-qin3, ZHANG Fu-quan4   

  1. 1 College of Computer Science,Guangdong Business and Technology University,Zhaoqing,Guangdong 526000,China
    2 Center for International Education,Philippine Christian University,Manila 1004,Philippine
    3 College of Computer Science and Engineering,South China University of Science and Engineering,Guangzhou 510000,China
    4 College of Computer Science,Beijing Institute of Technology University,Beijing 100081,China
  • Received:2022-03-27 Revised:2022-05-19 Online:2022-11-15 Published:2022-11-03
  • About author:PAN Hui-ping,born in 1982,Ph.D,associate professor.Her main research interests include image processing,artificial intelligence and digital media technology.
  • Supported by:
    General Project of the National Natural Science Foundation of China of the(61871204) and Guiding Project of Fujian Provincial Department of Science and Technology(2018H0028).

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Abstract: Traffic sign detection and recognition is the core function of automatic driving system.In order to identify traffic signs in real time and accurately,a method is improved on the basis of YOLO-V4 and combined with the spatial pyramid pool(SPP) module.Firstly,to increase the resolution and receptive field,the resolution of the three scales of the original feature map is changed to 26×26 and 52×52.Then,SPP module is added to the connection layer to eliminate the constraints of the network on the fixed scale,obtain the optimal characteristics in the maximum pooling layer and improve the network performance.Experiment uses the tachograph to collect various traffic sign images,compared with other excellent methods,the proposed method achieves better performance.The average detection and recognition accuracy of the proposed method is 99.0%,and the average detection time is 0.449 s,which meets the requirements of real-time detection.

Key words: Traffic sign recognition, Receptive field, YOLO-V4, Maxpooling, Spatial pyramid pool, Resolution

CLC Number: 

  • TP391
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