汽车工程学报2024，Vol.14Issue(5)：821-828,8.DOI:10.3969/j.issn.2095‒1469.2024.05.08
基于改进YOLOv5的轻量级汽车挡风玻璃雨滴目标检测模型
Improved Lightweight YOLOv5-Based Model for Raindrop Target Detection on Automotive Windshields
摘要
Abstract
In existing vision-based intelligent wiper systems,the raindrop target detection model has a large number of parameters and excessive computational complexity,making it challenging to deploy in vehicle embedded devices.To address these issues,the paper proposes a lightweight raindrop target detection model,YOLOv5-RGA.By integrating the RepVGG and GhostBottleneck modules to replace the convolution and C3 modules of the backbone network,we enhance the network's feature extraction capabilities while significantly reducing the parameters and computational load.Furthermore,adopting the Adam optimizer results in faster convergence and improves the average accuracy of the network model.Through experimental validation,compared with the YOLOv5s model,the YOLOv5-RGA model achieves a 0.8％ increase in average accuracy.Additionally,the number of model parameters is reduced by 48.5％,computation demand decreases by 35.2％,and the model size shrinks by 44.4％.The adoption of the lightweight raindrop target detection model effectively reduces hardware overhead and also facilitates model deployment.关键词
轻量化/改进YOLOv5/雨滴目标检测/RepVGG模块Key words
lightweight/improved YOLOv5/raindrop target detection/RepVGG module分类
交通工程引用本文复制引用
江炜,张广冬,陈锦华,宋树权..基于改进YOLOv5的轻量级汽车挡风玻璃雨滴目标检测模型[J].汽车工程学报,2024,14(5):821-828,8.基金项目
江苏省产学研合作项目(BY2020356) （BY2020356）
