Journal of Xidian University

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Real-time detection and identification of speed limit traffic signs under the BP neural network

ZHANG Xingguo1;LIU Xiaolei1;LI Jing1;WANG Huandong2   

  1. (1. Chinese Flight Test Establishment, Xi'an 710089, China;
    2. School of Remote Sensing and Information Engineering, Wuhan Univ., Wuhan 430072, China)
  • Received:2017-11-06 Online:2018-10-20 Published:2018-09-25

Abstract:

There are a lot of traffic signs based on the picture system of traffic sign recognition, and based on the video data to detect and identify the speed of traffic signs, However, there is a higher error rate. So this paper realizes the automatic detection and localization of speed limit traffic signs, and uses the BP neural network to identify the road signs. Meanwhile, the CamShift method and the optical flow method are used to speed up video, Experiment shows that the algorithm proposed in this paper can shorten the time by more than 50%, and the detection and recognition accuracy is over 90%, which is suitable for the related field of intelligent recognition.

Key words: traffic signs of speed limit, automatic detection and location, propaqation artificial neural network, video acceleration