Edge-Enhanced CrackNet for Underwater Crack Detection in Concrete Dams

Underwater crack detection in dam structures is of significant importance to ensure structural safety, assess operational conditions, and prevent potential disasters. Traditional crack detection methods face various limitations when applied to 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 study 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), and various complex underwater environments were simulated to construct a test dataset. Then, an improved Cycle-GAN image style transfer method was used to enhance the underwater images. Unlike conventional GAN-based underwater image enhancement methods that focus on global visual quality, our model specifically constrains edge preservation and high-frequency crack textures, providing a novel solution tailored for crack detection tasks. 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. The 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 (precision = 0.995, F1 = 0.99762, [email protected] = 0.995, and [email protected]:0.95 = 0.736) and providing a feasible technological solution for intelligent inspection of high dam underwater cracks.

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Publication Details

Journal
Applied Sciences
Published
2025-09-23
DOI
https://doi.org/10.3390/app151910326
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
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article

Edge-Enhanced CrackNet for Underwater Crack Detection in Concrete Dams

Weibo Zhang, Guangze Shen, Jinbao Sheng, Xiaobian Wu
Applied Sciences
Infrastructure Maintenance and Monitoring
article

Edge-Enhanced CrackNet for Underwater Crack Detection in Concrete Dams

Weibo Zhang, Guangze Shen, Jinbao Sheng, Xiaobian Wu
article en

Abstract

Underwater crack detection in dam structures is of significant importance to ensure structural safety, assess operational conditions, and prevent potential disasters. Traditional crack detection methods face various limitations when applied to 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 study 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), and various complex underwater environments were simulated to construct a test dataset. Then, an improved Cycle-GAN image style transfer method was used to enhance the underwater images. Unlike conventional GAN-based underwater image enhancement methods that focus on global visual quality, our model specifically constrains edge preservation and high-frequency crack textures, providing a novel solution tailored for crack detection tasks. 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. The 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 (precision = 0.995, F1 = 0.99762, [email protected] = 0.995, and [email protected]:0.95 = 0.736) and providing a feasible technological solution for intelligent inspection of high dam underwater cracks.

Applied SciencesVol. 15(19)
Nanjing Hydraulic Research Institute (CN), NARI Group (China) (CN), Ministry of Water Resources of the People's Republic of China (CN)
Openalex Percentile: Top 73%
Infrastructure Maintenance and Monitoring
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