An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network

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 environment...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-09, Vol.24 (18), p.5981
Main Authors: Huang, Xinbo, Liang, Chenxi, Li, Xinyu, Kang, Fei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
bilateral filtering denoising
Cracks
Dams
Datasets
Deep learning
Detectors
Image processing
Neural networks
Optimization
underwater crack detection
Unmanned aerial vehicles
Water
white balance method
YOLOv9-OREPA
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24185981