With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.
Keywords: CBAM; YOLOv5; adaptive color levels; adverse weather; transformer.