BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation

As global climate change intensifies, the frequency of extreme weather events, including urban flooding, has risen, posing a significant threat to the safety of urban areas. Efficient and precise flood depth monitoring is crucial for a dynamic understanding of disaster conditions, enabling informed decision-making and reducing the potential for significant loss of life and property. Although object detection models have shown promise in estimating flood depths and have achieved notable results, enhancements are still needed in applications across multi-scene and multi-scale. This research enhances the YOLOv8 model by integrating Bidirectional Feature Pyramid Network (BiFPN), Effective Squeeze and Excitation (EffectiveSE), and Wise-IoU (WIoU), targeting submerged cars to develop the BEW-YOLOv8 model. Testing shows that the BEW-YOLOv8 model improves Precision, Recall, F1 Score, mAP@0.5, and mAP@0.5-0.95 by 14.1%, 2.2%, 7.9%, 8.7%, and 6.3%, respectively, compared to the unmodified YOLOv8 model. Compared with other existing models, BEW-YOLOv8 achieves the best performance with less than one-tenth of the parameters. Furthermore, this study verifies that the BEWYOLOv8 model holds considerable promise for further enhancements with dataset expansions. Its precision and dependability make the BEW-YOLOv8 model well-suited for real-time flood depth assessment across multiple scenes and scales. The model's capability for real-time processing supports urgent flood response needs, offering solid technical assistance for managing urban floods and mitigating disaster risks.