Feature Adaptive YOLO for Remote Sensing Detection in Adverse Weather Conditions

Target detection in remote sensing has been one of the most challenging tasks in the past few decades. However, the detection performance in adverse weather conditions still needs to be satisfactory, mainly caused by the low-quality image features and the fuzzy boundary information. This work proposes a novel framework called Feature Adaptive YOLO (FA-YOLO). Specifically, we present a Hierarchical Feature Enhancement Module (HFEM), which adaptively performs feature-level enhancement to tackle the adverse impacts of different weather conditions. Then, we propose an Adaptive receptive Field enhancement Module (AFM) that dynamically adjusts the receptive field of the features and thus can enrich the context information for feature augmentation. In addition, we introduce Deformable Gated Head (DG-Head) which reduces the clutter caused by adverse weather. Experimental results on RTTS and two synthetic datasets demonstrate that our proposed FA-YOLO significantly outperforms other state-of-the-art target detection models.