Research on Traffic Sign Recognition in Night Environment based on Deep Learning
Ranran Liang, Tao Ning, Jia Xiangkun · 2022
In order to decrease in the accuracy of traffic sign recognition due to dim light at night, a novel YOLOv5 algorithm is proposed in this paper, which adds the Improved Adaptive Histogram Equalization (IAHE) method to image pre-processing, adjusts image brightness and contrast to highlight the related information about traffic signs. In view of the higher requirements of driving assistance system on the processing speed of recognition model and the standard convolution mode of backbone network is improved to the depth separable convolution method for model lightweight processing. To address the problem of wrong and missed detection generated by prediction box suppression, the conventional NMS is replaced by Weighted Boxes Fusion WBF to generate prediction box. Experiments have demonstrated that the improved algorithm has improved detection accuracy and decreased processing time for a single image compared with the original YOLOv5 algorithm in the self-built night environment dataset.