An adaptive traffic sign recognition scheme based on deep learning in complex environment

Abstract

Since detection and recognition of traffic signs is one of three modules of autonomous driving, it has been concerned by many researchers. But the traffic sign images, which are gathered in complex environments such as bright or foggy weather, are often obscured or blurred. This may bring about serious challenge to recognize traffic sign. In this paper, a novel and adaptive traffic sign recognition scheme is proposed, which addresses the issue of traffic sign recognition in complex environments. First, according to the environmental conditions, the traffic sign images are classified into four categories by Swin Transformer. Then, based on INDANE and ACE algorithms, an adaptive dataset enhancement algorithm is proposed to enhance the classified dataset and strengthen the image features. Finally, yolov5 algorithm is improved to effectively accomplish traffic sign recognition by reducing the maximum down sampling multiple, adjusting the stacking proportion of residual blocks, increasing the convolution kernel, and replacing the normalization method. Based on the GTSRB dataset, the experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the mAP of our proposed scheme is 99.86% when traffic sign is recognized in complex environment. Compared with the existing schemes, our proposed scheme has higher recognition accuracy and wider application.