Improved Lightweight YOLOv5-Based Model for Raindrop Target Detection on Automotive Windshields
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.
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