Road Damage Detection Method Based on UAV Imagery and YOLO-SCX

Haoyun Yang¹Zhengfeng Ma^1*Haiyan Zhu¹Qianmi Yu²

• ¹Qinghai Nationalities University, Xining, China
• ²Ningbo Huadong Nuclear Industry Investigation & Design Institute Group Co., Ltd, Ningbo, China

Automated road damage detection using Unmanned Aerial Vehicle (UAV) imagery is technically constrained by small target dimensions and complex environmental backgrounds. To address these issues within a low-computational budget, this study proposes YOLO-SCX, a computationally efficient object detection architecture based on the YOLOv5n baseline. The methodological novelty of this work lies in the systematic integration of three structural optimizations designed for aerial sensing: (1) the Convolutional Block Attention Module (CBAM) to suppress background noise; (2) a Grouped SPPCSPC module to strengthen multi-scale feature fusion; and (3) a Decoupled Head to independently optimize classification and regression tasks. The research utilizes a composite dataset of 1,500 images derived from UAV-RDD and CrackForest sources, rigorously partitioned into training (1,000), validation (250), and testing (250) sets. Experimental results on the held-out test set demonstrate that YOLO-SCX achieves a mean Average Precision (mAP@0.5) of 66.3% and Precision of 79.2%, representing absolute improvements of 5.8% and 6.0% respectively over the baseline. Furthermore, the model maintains an inference speed of 185 FPS with 8.7 million parameters, confirming its suitability for real-time edge deployment compared to heavier architectures like YOLOv7 and YOLOv8.