Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 325-330.doi: 10.11896/jsjkx.210300117

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Traffic Sign Detection Based on MSERs and SVM

HU Cong, HE Xiao-hui, SHAO Fa-ming, ZHANG Yan-wu, LU Guan-lin, WANG Jin-kang   

  1. College of Field Engineering,Army Engineering University,Nanjing 210007,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:HU Cong,born in 1996,postgraduate.His main research interests include computer vision and object detection.
    HE Xiao-hui,born in 1975,professor.His main research interests include mechatronics and deep learning.
  • Supported by:
    National Natural Science Foundation of China(61671470).

Abstract: Traffic sign detection is an important research content in the field of vehicle assistant driving system and automatic driving.It can instantly assist drivers or automatic driving systems to detect and identify traffic signs effectively.Based on this requirement,a traffic sign detection method based on real traffic scene is proposed.Firstly,the appropriate database is selected to convert the road scene image in the database into gray-scale image,and the gray-scale image is processed by simplified Gabor filtering to enhance the edge information of traffic signs.Secondly,the region recommendation algorithm MSERs is used to process the Gabor filtered feature map to form the proposal region of traffic signs.Finally,by extracting hog features,SVM is used for classification.Through experiments,the feature extraction performance of simplified Gabor filter,the performance of SG-MSERs region recommendation and filtering are analyzed,and the classification accuracy and processing time of the algorithm are obtained.The results show that the algorithm achieves good detection performance on both GTSDB and CSTD datasets,and basically meets the needs of real-time processing.

Key words: HOG, MSERs, Simplified Gabor filters, SVM, Traffic sign detection

CLC Number: 

  • TP301.6
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