Huang Xinbo, Liang Chenxi, Li Xinyu, Kang Fei
School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China.
China Institute of Water Resources and Hydropower Research, Beijing 100048, China.
Sensors (Basel). 2024 Sep 15;24(18):5981. doi: 10.3390/s24185981.
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.
水下裂缝难以检测和观测，这给裂缝检测带来了重大挑战。目前，基于深度学习的水下裂缝检测方法严重依赖大量裂缝图像，而由于水下环境复杂且危险，这些图像很难收集。本研究提出了一种新的水下图像处理方法，该方法结合了一种新颖的白平衡方法和双边滤波去噪方法，将水下裂缝图像转换为具有原始裂缝特征的高质量水上图像。然后基于改进的YOLOv9-OREPA模型进行裂缝检测。通过实验发现，与其他方法相比，本研究提出的新图像处理方法显著提高了新图像的评估指标。改进后的YOLOv9-OREPA性能也有显著提升。实验结果表明，本研究提出的方法是一种适用于检测大坝水下裂缝的新方法，实现了将水下图像转换为水上图像的目标。
