ArticleSensors (Basel, Switzerland)2024
An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep-AquATTEN: a hybrid multi-module attention architecture for robust fish detection and classification in turbid underwater environments.Scientific reports · 2026Article
- Enhancing multilevel tea leaf recognition based on improved YOLOv8n.Frontiers in plant science · 2025Article
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Authors and funding
4 authors.
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Abstract
Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater environments. This study proposes a new underwater image-processing method that combines a novel white balance method and bilateral filtering denoising method to transform underwater crack images into high-quality above-water images with original crack features. Crack detection is then performed based on an improved YOLOv9-OREPA model. Through experiments, it is found that the new image-processing method proposed in this study significantly improves the evaluation indicators of new images, compared with other methods. The improved YOLOv9-OREPA also exhibits a significantly improved performance. The experimental results demonstrate that the method proposed in this study is a new approach suitable for detecting underwater cracks in dams and achieves the goal of transforming underwater images into above-water images.
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