Integrated UAV Path Planning and Attention-Enhanced Instance Segmentation for Automated Infrastructure Surface Defect Detection

This paper presents an integrated framework for automated detection of surface defects in infrastructures using unmanned aerial vehicles (UAVs), comprising 3D model-based adaptive path planning, high-resolution image acquisition, and an attention-enhanced instance segmentation model. However, existing approaches face two key limitations: (i) conventional UAV path planning lacks adaptive trajectory correction for non-horizontal bridge geometries, and (ii) instance segmentation models for infrastructure defects have not been systematically optimized for both accuracy and edge-device deployability. To address these gaps, the proposed framework was trained on 3625 annotated images covering two defect categories (spalling and cracking) and preliminarily validated through a proof-of-concept field study on a concrete viaduct section where seven spalling and one reinforcement exposure were detected. The methodology consists of three core components: (i) 3D model-based adaptive path planning, (ii) high-resolution image acquisition under variable infrastructure geometries, and (iii) an improved instance segmentation model based on YOLOv8-seg. To ensure consistent imaging geometry, a segmented linear interpolation method is introduced to adaptively correct flight trajectories for non-horizontal infrastructure sections. For damage detection, we propose a structurally enhanced YOLOv8-seg model, denoted as YOLOv8-seg-ECAC2f-all, which integrates Efficient Channel Attention (ECA) modules into a fully modified backbone architecture. Compared to the baseline YOLOv8-seg, the proposed model achieves a mean average precision (mAP50–95) of 91.8% for bounding box detection and 61.7% for instance segmentation, corresponding to improvements of 7.7% and 2.8%, respectively. The framework was preliminarily validated on a concrete viaduct of the Guangfojiangzhu Expressway, achieving 100% inspection coverage and detection of all eight ground-truth surface defects (seven spalling and one reinforcement exposure) in this pilot study, including two minor spalling cases (≤0.5 m2) missed by manual inspection. These results demonstrate the technical feasibility of the proposed framework for real-world concrete bridge inspection and its potential for reducing manual inspection effort while improving detection sensitivity for minor defects (mAP50–95).