Transformer-based vehicle detection algorithm under foggy conditions

An efficient foggy weather vehicle recognition framework is proposed to address the challenges of vehicle identification under adverse weather conditions in this study. Enhanced high-resolution data is used as input, and an image restoration model is employed to effectively capture multi-scale feature information. Additionally, an image dehazing model is incorporated to restore clear vehicle features from foggy and blurred images, thereby improving the accuracy of vehicle detection in foggy conditions. In the experimental analysis, visualized results are first presented, and the model's performance is analyzed by comparing it with the detection results of other algorithms. The data is further analyzed, with several dehazing algorithms integrated into the vehicle detection model for ablation experiments to validate the model's effectiveness.