Traditional bridge inspections are often characterized by low efficiency, high costs, and notable safety risks. To address these challenges, this paper presents an integrated unmanned aerial vehicle (UAV) -based system for automated defect detection. A key contribution of this work is the development of a holistic, application-driven solution that combines a custom UAV platform with RCO-YOLOv5, a specialized, lightweight deep learning model. RCO-YOLOv5 is methodically optimized from the YOLOv5s framework to achieve a superior balance of accuracy and efficiency for real-world deployment. Architectural enhancements include an efficient RepC2f backbone, an attention-enhanced C3MSA neck, adaptive Omni-Dimensional Dynamic Convolution (ODConv), and the normalized Wasserstein distance (NWD) loss function. We detail the design and implementation of our UAV system, which streams live video for real-time processing on a ground station. Comprehensive experiments demonstrate that RCO-YOLOv5 achieves a mean average precision (email protected) of 91. 0%, a 4. 0% increase over its baseline, while being 12. 8% smaller and 6. 7% faster. The successful field testing of the integrated system confirms its potential as an effective tool for modern structural health monitoring.
Chen et al. (Mon,) studied this question.