基于YOLOv5n-BGF的雾天道路目标检测算法OA

There are difficulties in the road object detection on foggy days,such as feature extraction and balancing accuracy and efficiency.In view of this,the study innovatively proposes an efficient and lightweight variant based on YOLOv5n,and the variant is named YOLOv5n-BGF.This variant incorporates a bi-directional feature pyramid network(BiFPN)model,which leverages the structural characteristics of bidirectional connections to effectively integrate features of different scales.Furthermore,a GELAN module is introduced to replace the C3 structure in the neck network,which enhances the extraction of valid features while reducing computational load.Finally,on the basis of taking account of the bounding box regression of different samples,Focaler-IoU is employed to improve the detection performance.The proposed model has been validated on a local platform on the private foggy road object detection dataset D-8800.Experimental results indicate that in comparison with the base model YOLOv5n,the mAP@0.5 of the YOLOv5n-BGF is increased by 5.3%,its parameter count is reduced by 25%,yet its GFLOPs is only 3.5.With its exceptional performance,the YOLOv5n-BGF is superior to the other object detection models on the foggy road object detection dataset D-8800.To sum up,it provides an efficient solution for foggy road object detection.