Ensuring the safety of water networks is a research hotspot in the current water conservancy industry, and dams are an important part. However, over time, the dam is prone to varying degrees of aging and disease, most of which are structural cracks. If they cannot be discovered and repaired in time, the normal operation of the dam will be affected, and even catastrophic accidents such as dam failure will occur. However, complex backgrounds and blurred images can easily lead to misjudgments by machine vision detection models, and high‐efficiency and accurate detection and evaluation technology are urgently needed. This paper combines the deep semantic segmentation network and the model hyperparameters optimization algorithm to propose a data‐intelligent perception method of dam underwater cracks driven by knowledge coupling. Taking the underwater detection of a concrete face rockfill dam as an example, the effectiveness of the model is verified by using the underwater vehicle as the carrier. Experimental results indicate that the developed method achieves an intersection‐union ratio of 0.9301, a precision rate of 0.9678, a precision rate of 0.9472, and a recall rate of 0.9577 in the test set. This shows that the constructed method has a high crack fine detection performance. In addition, the developed method has better segmentation performance in different complex underwater crack scenes, which further illustrates the high performance of the developed method.

中文翻译：

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基于机器视觉的群体优化算法和深度学习的大坝水下裂缝智能分割方法


确保水网安全是当前水利行业的研究热点，而大坝就是重要组成部分。但随着时间的推移，大坝很容易出现不同程度的老化和病害，其中大部分是结构裂缝。如果不能及时发现和修复，就会影响大坝的正常运行，甚至发生溃坝等灾难性事故。然而复杂的背景和模糊的图像很容易导致机器视觉检测模型的误判，迫切需要高效、准确的检测评估技术。本文结合深度语义分割网络和模型超参数优化算法，提出一种知识耦合驱动的大坝水下裂缝数据智能感知方法。以混凝土面板堆石坝水下检测为例，以水下机器人为载体验证了模型的有效性。实验结果表明，该方法在测试集中达到了0.9301的交并比、0.9678的准确率、0.9472的准确率和0.9577的召回率。这说明所构建的方法具有较高的裂纹精细检测性能。此外，所开发的方法在不同复杂的水下裂缝场景中具有更好的分割性能，这进一步说明了所开发的方法的高性能。