Traffic Sign Recognition Based on Image Enhancement and SKNet

Abstract

Abstract: Aiming at the problems that the existing traffic sign recognition systems extract image feature insufficiently and are difficult to recognize under complex situations， a traffic sign recognition model HE-SKNet is designed based on image enhancement and SKNet. Firstly， the histogram equalization is used to enhance the images of traffic signs with too bright or too dark. Then the SKNet network that adaptively adjusts the size of the receptive field is used for feature extraction and classification. The experimental results on the GTSRB dataset show that the recognition accuracy of the proposed HE-SKNet model reaches 98.95%， which enjoys 2.77 percentage points higher than that of ResNet， ResNeXt， SENet and SKNet on average. It verifies that the HE-SKNet model adaptively extracts different scales of feature and is more suitable for complex practical application scenarios with too bright or too dark.

Key words: image enhancement, histogram equalization, SKNet, deep learning, traffic sign recognition

LIAO Cong , GUO Huang, ZHAO Mao-jun, WANG Yu-song, BAI Jun-feng. Traffic Sign Recognition Based on Image Enhancement and SKNet[J]. Computer and Modernization, 2023, 0(03): 23-28.

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