Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (4): 68-72.doi: 10.16180/j.cnki.issn1007-7820.2019.04.015

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A Multi-environmental Traffic Sign Recognition Model

YAN Maosen,CHEN Jiaqi   

  1. School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2018-03-18 Online:2019-04-15 Published:2019-03-27
  • Supported by:
    Research and Innovation Project Fund of the Teaching Committee,Shanghai(12zz146)


Traffic sign recognition plays an important role in autopilot and assisted driving systems. However, most related studies were limited to the identification of daylight scenarios. If these models were applied in night, the difference of light intensity would lead to a significant decrease in recognition accuracy. In order to solve this problem, this paper proposed a light intensity classification model, which used different methods to identify the signboard according to the difference of light intensity to ensure excellent recognition rates in night. Firstly, the model divided the scene and processed the ROI by different methods. And then, eigenvectors were trained by KNN and SVM. Finally, the trained eigenvectors could identify the type of a sign. Experiments proved that the accuracy rate of this model in different environments was as high as 98.1%.

Key words: multiple thresholding, binary images, ROI, SVM, log polar, NCC

CLC Number: 

  • TP317.4