AI-Based Vision for Hydraulic-Structure Inspection A DamCrack Feasibility Study

Abstract

Reliable management of dams, spillways, canals, tunnels, and related hydraulic assets depends on timely visual evidence about deterioration. In many institutions, inspection is still based mainly on human observation, which may be slow, uneven between inspectors, and risky in locations that are high, confined, submerged, or difficult to access. This study develops a computer-vision framework for responsible AI-supported inspection of hydraulic structures. The framework links image capture, data screening, YOLO-style detection, U-Net-style segmentation, explainable outputs, and risk-based prioritization within a human-reviewed reporting process. The manuscript deliberately avoids unsupported claims of high accuracy and instead emphasizes reproducible data handling, annotation control, leakage-aware splitting, qualitative error review, and transparent reporting. To make the approach testable, DamCrack, a public drone and smartphone dataset for concrete-dam damage assessment, is selected as an initial validation source. A limited CPU-based trial was conducted on 500 DamCrack detection image-label pairs to verify end-to-end execution. On the held-out test split, the trial obtained precision = 0.386, recall = 0.402, mAP@0.5 = 0.340, and mAP@0.5:0.95 = 0.163. These values are reported as a feasibility baseline rather than as operational performance. They indicate that the workflow can be executed and audited, while larger data splits, GPU-based training, segmentation validation, and expert review remain necessary before field-deployment claims are made. The contribution is an inspection architecture and a practical validation route grounded in a real public dam-damage dataset.