Robust scale fusion and edge-aware feature attention network for remote sensing UAV road detection under harsh weather

Accurate road detection from UAV imagery under adverse weather remains a significant challenge due to reduced visibility, motion blur, and environmental interference. To address these issues, we propose RSFC-EAFANet, a robust detection framework that integrates Robust Scale Fusion Convolution (RSFC) with an Edge-aware Adaptive Feature Aggregation (EAFA) module. RSFC dynamically adjusts convolution kernels to better handle scale variation and degraded visual inputs, while EAFA enhances edge information and suppresses noise in challenging environments. To support evaluation under realistic conditions, we construct a dedicated UAV-based road inspection dataset comprising 19,832 images collected across diverse weather scenarios such as rain, snow, and fog. This dataset features fine-grained annotations and provides a valuable benchmark for assessing detection performance in adverse environments. Extensive experiments on this dataset show that RSFC-EAFANet achieves a mean Average Precision (mAP) of 58.48%, outperforming strong baselines such as NAS-FPN and MegDet by a clear margin. The model also maintains a competitive inference speed of 23.2 FPS, demonstrating a strong balance between accuracy and efficiency. These results highlight both the effectiveness of RSFC-EAFANet and the importance of weather-aware benchmarks for advancing UAV-based road detection. It has made significant contributions to applications in real-world scenarios such as intelligent traffic monitoring, road safety early warning, and road inspection under extreme weather conditions.