YOLOv13-Based Two-Stage Framework for Underwater Damage Detection on Reinforced Concrete Surfaces

Abstract

Prolonged underwater exposure degrades reinforced concrete (RC) structures, causing chloride-induced corrosion, spalling, cracking, and rebar exposure. Timely damage identification is critical for structural safety, but conventional non-destructive testing methods face severe limitations underwater due to restricted accessibility, image degradation, and weak-textured, irregular crack boundaries. Vision-based inspection offers a promising alternative but remains constrained by underwater optical degradation. This study proposes a two-stage detection framework for underwater RC based on YOLOv13 (YOLOv13-TSDD). First, an underwater color-detail enhancement network (UCDEN) performs color correction, detail recovery, and contour reconstruction through multi-channel color enhancement and multi-level feature refinement. Second, two detection modules are introduced: a pinwheel-shaped receptive field convolution (PRFConv), improving sensitivity to directional textures and local linear structural responses in shallow layers, and a crack-aware efficient multi-scale attention (CEMA) mechanism, enabling joint channel-spatial recalibration and multi-scale focus on crack-relevant regions. A fine-grained irregular crack IoU (FID-IoU) loss function is also developed, using auxiliary boundary boxes and piecewise weighted mapping to improve bounding-box regression for irregular cracks. Experimental results demonstrate that YOLOv13-TSDD not only achieves the best overall image enhancement performance among the evaluated methods but also delivers the highest detection performance. On the constructed underwater dataset, YOLOv13-TSDD achieves Precision, Recall, and mAP@0.50 of 93.63%, 88.97%, and 94.52%, respectively, demonstrating improved performance under complex underwater conditions.