MAG-YOLO: A Multi-Scale Anisotropic Gating-Aware Network for UAV-Based Pavement Distress Detection

Accurate identification of pavement distress in UAV imagery is crucial for road safety but is hindered by scale disparities, background noise, and isotropic extraction constraints that neglect highly directional distress textures. This paper proposes MAG-YOLO, a lightweight detection network driven by multi-scale and anisotropic gating perception. The architecture integrates three core innovations: a Re-parameterized Multi-scale Attention (RMA) module, which employs multi-branch depthwise convolutions to enhance fine-grained texture capture while mitigating parameter redundancy; an Anisotropic Gating Feature Pyramid Network (AGFPN), which incorporates orthogonal feature decomposition and dynamic gating mechanisms to extract multi-scale topological features and suppress environmental noise; and a Cross-stage Multi-scale Gated Linear Attention (CMGLA) module designed for global context aggregation and local feature recalibration. Extensive experiments on the RDD2022_China_Drone and UAPD datasets demonstrate that MAG-YOLO outperforms the YOLOv11n baseline by 5.4% and 7.0% in mAP50, respectively, while reducing the parameter count by 29.1%. Deployment tests on the RK3588 edge platform further confirm that MAG-YOLO achieves a favorable balance between accuracy and real-time efficiency. This work provides a robust and deployment-ready solution for UAV-based pavement distress detection, supporting automated road inspection and intelligent pavement maintenance.