Detection of Targets Affecting Driving Comfort on Improved YOLOv5
Current autonomous driving technology focuses on safety.With the development of autonomous driving technology,people's re-quirements for driving comfort will also continue to increase.A YOLOv5-based target detection improvement method is proposed for detecting small and medium-sized obstacles that affect driving comfort.In order to solve the problem that small and medium-sized obstacles affecting driving comfort are very similar to the background,the CA(Coordinate Attention)module is introduced,which improves the ability to extra the salient features of the target while keeping the model lightweight and improving the attention to the key information;The CIoU loss function is replaced by the α-IoU loss function as the bounding box regression loss function,which improves the optimization space for different levels of targets;The new convolution module is designed to retain the original features while incorporating deeper feature information and reducing the number of parameters.The experimental results show that the improved method improves the mAP(mean average precision)from 87.8%to 89.9%compared to the original YOLOv5 with the reduction of the number of parameters and GFLOPs,and the FPS of single image detection on GPU reaches 70,which is better than the comparison algorithm and improves the detection effect while satisfying the real-time perfor-mance.
deep learningobject detectionYOLOv5coordinate attention mechanismα-IoU
国家科技期刊平台
NSTL
万方数据
维普
