Wuyuntana Gongzhabayier

In unmanned aerial vehicle (UAV)-based monocular inspection, cracks typically present as geometrically asymmetric, elongated, low-contrast weak targets, making accurate segmentation and spatial localization challenging. Existing methods are susceptible to missed detections and false positives when handling slender cracks, and monocular 3D reconstruction for localization is often burdened by redundant frames, resulting in limited modeling efficiency. To mitigate these issues, we propose a high-precision framework for crack segmentation and spatial localization from UAV imagery. First, Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping, version 3 (ORB-SLAM3) is adopted for keyframe selection to suppress data redundancy and improve reconstruction stability. Second, we develop an enhanced YOLOv11-seg model by integrating the Dilation-wise Residual Segmentation (DWRSeg) module, the Weighted IoU (WIoU) loss, and the Lightweight shared convolutional separator batch-normalization detection head (LSCSBD) to strengthen feature discrimination and segmentation robustness for slender cracks, yielding high-quality crack masks. Finally, the predicted masks are projected onto the reconstructed 3D surface to obtain precise spatial localization. Our experimental results demonstrate that the proposed approach improves the segmentation mAP@50 by 7.2% over the baseline while reducing computational complexity from 10.2 to 9.8 GFLOPs. In addition, keyframe-based processing reduces the 3D modeling time by 59.4% compared to that with full-frame reconstruction. Overall, the proposed framework jointly enhances crack segmentation accuracy and substantially accelerates 3D modeling and localization, providing an effective solution for efficient UAV-based crack inspection.