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

AI Article Synopsis

  • Underwater crack detection is challenging due to the difficulty in collecting sufficient quality images in complex and hazardous underwater environments.
  • This study introduces a new image-processing method that combines a novel white balance technique with bilateral filtering to convert underwater crack images into high-quality above-water images while preserving original crack features.
  • The proposed method, when used with an improved YOLOv9-OREPA model, shows significant improvements in crack detection performance and evaluation metrics compared to existing methods, making it a promising approach for identifying underwater cracks in structures like dams.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436054PMC
http://dx.doi.org/10.3390/s24185981DOI Listing

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