Image-Level Domain Alignment for Real-Time Underwater Crack Detection Using YOLO with an ROV

Abstract

Underwater concrete infrastructure plays a crucial role in energy and water systems. However, it requires regular inspections to ensure structural integrity. Remotely Operated Vehicles (ROVs) offer a safer and more cost-effective alternative to diver-based inspections. The data collected during inspections often require extensive post-mission processing, either manually or through computationally intensive algorithms. This limitation makes real-time damage detection during inspections impossible. In this study, we present a real-time image-level domain alignment pipeline suitable for deployment on resource-constrained hardware. It combines image enhancement with crack detection using a YOLO11n-seg model fine-tuned on a publicly available aerial concrete crack dataset. The model was quantized and deployed on a Jetson Nano, which was connected to an ROV for real-time inference. To reduce the domain gap between the raw underwater images captured by the ROV and the aerial training data, a Contrast Limited Adaptive Histogram Equalization (CLAHE)-based strategy was applied. Field tests were conducted on a submerged concrete embankment in a turbid lake environment. A validation dataset was developed to evaluate performance offline and is publicly available.