DFA-YOLO: Deformable Spatial Attention and Hierarchical Fusion for Robust Object Detection in Adverse Weather

In complex real-world scenarios, object detection faces significant challenges due to severe noise interference and feature degradation. To overcome these limitations, this paper proposes DFA-YOLO, an enhanced YOLOv11 framework integrating three key innovations. First, a Deformable Spatial Attention (DSA) module is introduced into the C3k2 backbone blocks, which dynamically adjusts the receptive field to focus on informative spatial regions. This significantly enhances the model's adaptability to geometric variations and occluded objects. Second, a Hierarchical Multi-Scale Fusion Module (HMFM) is designed to dynamically recalibrate feature responses across scales, enhancing the model's perception of multi-scale targets. Third, an improved Wasserstein loss function combines small-object adaptive weighting with dynamic gradient modulation to address boundary ambiguity and scale sensitivity under adverse conditions. Extensive experiments on the RTTS dataset validate the superiority of our approach, achieving improvements of 3.4% and 2.8% in mAP50 and mAP50-95, respectively. Additional experiments on the Exdark dataset confirm the method's robust generalization capability, with significant accuracy gains observed across all benchmarks.