WaRENet: A Novel Urban Waterlogging Risk Evaluation Network

Abstract

In this article, we propose a novel urban waterlogging risk evaluation network (WaRENet) to evaluate the risk of waterlogging. The WaRENet distinguishes whether an urban image involves waterlogging by classification module, and estimates the waterlogging risk levels by multi-class reference objects detection module (MCROD). First, in the waterlogging scene classification, ResNet combined with Se-block is used to identify the waterlogging scene, and lightweight gradient-weighted class activation mapping (Grad-CAM) is also integrated to roughly locate overall waterlogging areas with low computational burden. Second, in the MCROD module, we detect reference objects, e.g., cars and persons in waterlogging scenes. The positional relationship between water depths and reference objects serves as risk indicators for accurately evaluating waterlogging risk. Specifically, we incorporate switchable atrous convolution (SAC) into YOLOv5 to solve occlusions and varying scales problems in complex waterlogging scenes. Moreover, we construct a large-scale urban waterlogging dataset called UrbanWaterloggingRiskDataset (UWRDataset) with 6,351 images for waterlogging scene classification and 3,217 images for reference objects detection. Experimental results on the dataset show that our WaRENet outperforms all comparison methods. The waterlogging scene classification module achieves accuracy of 95.99%. The MCROD module obtains mAP of 54.9%, while maintaining a high processing speed of 70.04 fps.