Deep Learning-Based Algorithm for Robust Object Detection in Flooded and Rainy Environments

Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically, the DCN module enhances the model’s adaptability to target deformation and noise interference by adaptively adjusting the sampling grid positions, while also integrating feature amplitude modulation to further improve robustness. Additionally, the MSDA module is introduced to capture contextual features across multiple scales, effectively addressing issues related to target occlusion and scale variation commonly encountered in flood-affected environments. Experimental evaluations are conducted on the ISE-UFDS and UA-DETRAC datasets. The results demonstrate that the proposed model significantly outperforms state-of-the-art methods in key evaluation metrics, including precision, recall, F1-score, and mAP (Mean Average Precision). Notably, the model exhibits superior robustness and generalization performance under simulated severe weather conditions, offering reliable technical support for disaster emergency response systems. This study contributes to enhancing the accuracy and real-time capabilities of flood early warning systems, thereby supporting more effective disaster mitigation strategies.