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

在现有的基于视觉的智能雨刮系统中,雨滴目标检测模型的参数量多,计算规模大,不利于部署到车载嵌入式设备中。针对上述问题,提出一种改进的轻量级雨滴目标检测模型YOLOv5-RGA。使用轻量化网络RepVGG模块和GhostBottleneck模块替代主干网络的卷积模块和C3模块,改善网络的特征提取能力,降低网络的参数量和计算量。使用Adam优化器代替SGD优化器,加快收敛速度,提高网络模型的平均精度。通过试验验证,与YOLOv5s模型相比,基于YOLOv5-RGA的雨滴目标检测模型的平均精度提高了0.8%,同时模型参数量降低了48.5%,计算量降低了35.2%,模型大小降低了44.4%。轻量级雨滴目标检测模型的应用能大大降低硬件开销,同时也有利于模型的部署。

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.