Road Traffic Waterlogging Detection Based on YOLOv5

In view of the frequent occurrence of waterlogging in urban areas and the problems that traditional waterlogging monitoring methods consume a lot of human and material resources with high cost and low timeliness, an improved YOLOv5 waterlogging detection method for road traffic is proposed, which enhances the feature extraction of road traffic waterlogging information by feature extraction of waterlogging in urban waterlogging scenarios, and adds the CBAM attention mechanism in the backbone network; and adds a CIoU loss function to optimize the model in the prediction layer to improve the identification accuracy of road traffic waterlogging so as to construct a road traffic waterlogging detection model. In the prediction layer, a CIoU loss function is added to optimize the model and improve the detection accuracy of road water, thus constructing a road water detection model. By screening 5000 road traffic waterlogging images on the public dataset RSCD for training, the experimental results show that the average accuracy of the method is 84.4%, which is 3.7% higher than the original YOLOv5 algorithm, and it can more accurately extract and identify the waterlogged area of the image automatically, which can pave the way for further development of related research, and provide technical support for urban waterlogging monitoring and emergency management. The method can pave the way for further related research and provide technical support for urban flood monitoring and emergency management.