软件导刊2024，Vol.23Issue(1)：143-149,7.DOI:10.11907/rjdk.231095
改进YOLOv5的影响驾乘舒适性目标检测
Detection of Targets Affecting Driving Comfort on Improved YOLOv5
李澍祺 1刘堂友1
作者信息
- 1. 东华大学 信息科学与技术学院, 上海 201620
- 折叠
摘要
Abstract
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.关键词
深度学习/目标检测/YOLOv5/坐标注意力机制/α-IoUKey words
deep learning/object detection/YOLOv5/coordinate attention mechanism/α-IoU分类
信息技术与安全科学引用本文复制引用
李澍祺,刘堂友..改进YOLOv5的影响驾乘舒适性目标检测[J].软件导刊,2024,23(1):143-149,7.
