关键词: 无人机图像, 路面病害, 边缘感知, 频率特征融合, 特征金字塔网络

Abstract: To address insufficient multi-scale feature fusion and edge detail loss in Unmanned Aerial Vehicle (UAV) images, an improved YOLO12n-based Edge and Frequency Aware Network (EFA-Net) was proposed. Firstly, an edge-aware feature extraction module, Edge-Aware Stem (EAStem), was designed to enhance fine-grained edge and low-level structural feature extraction. Secondly, a frequency-aware cross-stage partial module, Frequency-Aware C2f (FAC2f), was proposed to enhance the model's perception and recognition of complex distresses by efficiently fusing spatial and frequency domain features. Finally, a Multi-source collaborative aggregation enhanced Feature Pyramid Network (MFPN) was constructed to mitigate semantic information loss and optimize cross-layer feature alignment. Experimental results on the UAV-PDD2023 dataset demonstrate that EFA-Net achieves mAP50 and mAP50-95 of 80.66% and 55.54%, respectively. The model attains superior detection accuracy while maintaining low computational complexity.

Key words: Unmanned Aerial Vehicle (UAV) image, pavement distress, edge awareness, frequency feature fusion, Feature Pyramid Network (FPN)