Road target detection algorithm based on improved YOLOv5
Aiming at the problems of low recognition accuracy and easy missed detection of target detection algorithms in foggy weather,a new algorithm based on the YOLO v5 framework is proposed to remove fog first and detect later.In this paper,the improved AOD-Net defogging algorithm is used for image enhancement;Use the Twin-transform Block module to replace and extract small and medium target features;At the same time,the CBM A attention mechanism was added to enhance the model's ability to extract image features.Adopting DIoU_NMS post-processing method,accurate regression prediction box.The experimental results show that compared to the original YOLOv5s network,the accuracy of the proposed improvement scheme is significantly improved on the BDD100K dataset,with an increase of 1.59%in the haze scenario and 5.80%in the dense fog scenario.Experimental results show that the algorithm proposed in this paper can effectively improve the problem of missed detection in target recognition under foggy conditions.