Edge Enhanced CrackNet for Underwater Crack Detection of Concrete Dams

Underwater crack detection in dam structures is of significant engineering importance and scientific value for ensuring the structural safety, assessing operational conditions, and preventing potential disasters. Traditional crack detection methods face various limitations when applied in underwater environments, particularly in high dam underwater environments where image quality is influenced by factors such as water flow disturbances, light diffraction effects, and low contrast, making it difficult for conventional methods to accurately extract crack features. This paper proposes a dual-stage underwater crack detection method based on Cycle-GAN and YOLOv11, called Edge Enhanced Underwater CrackNet (E2UCN), to overcome the limitations of existing image enhancement methods in retaining crack details and improving detection accuracy. First, underwater concrete crack images were collected using an underwater remotely operated vehicle (ROV), simulating various complex underwater environments to construct a test dataset. Then, an improved Cycle-GAN image style transfer method was used to enhance the underwater images, emphasizing crack edges and high-frequency details, avoiding the issues of blurred crack edges and detail loss in existing underwater image enhancement methods. Subsequently, the YOLOv11 model was employed to perform object detection on the enhanced underwater crack images, effectively extracting crack features and achieving high-precision crack detection. Experimental results show that the proposed method significantly outperforms traditional methods in terms of crack detection accuracy, edge clarity, and adaptability to complex backgrounds, effectively improving underwater crack detection accuracy and providing a feasible technological solution for intelligent inspection of high dam underwater cracks.