Robust Object Detection for UAVs in Foggy Environments with Spatial-Edge Fusion and Dynamic Task Alignment

Robust scene perception in adverse environmental conditions, particularly under dense fog, presents a persistent and fundamental challenge to the reliability of object detection systems. To address this critical challenge, we propose Fog-UAVNet, a novel lightweight deep-learning architecture designed to enhance unmanned aerial vehicle (UAV) object detection performance in foggy environments. Fog-UAVNet incorporates three key innovations: the Spatial-Edge Feature Fusion Module (SEFFM), which enhances feature extraction by effectively integrating edge and spatial information, the Frequency-Adaptive Dilated Convolution (FADC), which dynamically adjusts to fog density variations and further enhances feature representation under adverse conditions, and the Dynamic Task-Aligned Head (DTAH), which dynamically aligns localization and classification tasks and thus improves overall model performance. To evaluate the effectiveness of our approach, we independently constructed a real-world foggy dataset and synthesized the VisDrone-fog dataset using an atmospheric scattering model. Extensive experiments on multiple challenging datasets demonstrate that Fog-UAVNet consistently outperforms state-of-the-art methods in both detection accuracy and computational efficiency, highlighting its potential for enhancing robust visual perception under adverse weather.