Automated crack detection based on image processing is widely used when inspecting concrete structures. The existing methods for crack detection are not yet accurate enough due to the difficulty and complexity of the problem; thus, more accurate and practical methods should be developed. This paper proposes an automated crack detection method based on image processing using the light gradient boosting machine (LightGBM), one of the supervised machine learning methods. In supervised machine learning, appropriate features should be identified to obtain accurate results. In crack detection, the pixel values of the target pixels and geometric features of the cracks that occur when they are connected linearly should be considered. This paper proposes a methodology for generating features based on pixel values and geometric shapes in two stages. The accuracy of the proposed methodology is investigated using photos of concrete structures with adverse conditions, such as shadows and dirt. The proposed methodology achieves an accuracy of 99.7%, sensitivity of 75.71%, specificity of 99.9%, precision of 68.2%, and an F‐measure of 0.6952. The experimental results demonstrate that the proposed method can detect cracks with higher performance than the pix2pix‐based approach. Furthermore, the training time is 7.7 times shorter than that of the XGBoost and 2.3 times shorter than that of the pix2pix. The experimental results demonstrate that the proposed method can detect cracks with high accuracy.

中文翻译：

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两步光梯度增强机自动检测混凝土表面图像中的裂缝

在检查混凝土结构时，广泛使用基于图像处理的自动裂缝检测。由于问题的难度和复杂性，现有的裂纹检测方法还不够准确。因此，应该开发出更准确和实用的方法。本文提出了一种基于图像处理的自动裂缝检测方法，该方法是使用有监督的机器学习方法之一的光梯度增强机（LightGBM）。在有监督的机器学习中，应确定适当的功能以获得准确的结果。在裂纹检测中，应考虑目标像素的像素值和线性连接时出现的裂纹的几何特征。本文提出了一种在两个阶段基于像素值和几何形状生成特征的方法。使用带有不利条件（例如阴影和污垢）的混凝土结构照片研究了所提出方法的准确性。所提出的方法达到了99.7％的准确度，75.71％的灵敏度，99.9％的特异性，68.2％的精确度以及0.6952的F值。实验结果表明，与基于pix2pix的方法相比，该方法能够以更高的性能检测裂纹。此外，训练时间比XGBoost短7.7倍，比pix2pix短2.3倍。实验结果表明，该方法能够高精度检测裂纹。灵敏度为75.71％，特异性为99.9％，精度为68.2％，F值为0.6952。实验结果表明，与基于pix2pix的方法相比，该方法能够以更高的性能检测裂纹。此外，训练时间比XGBoost短7.7倍，比pix2pix短2.3倍。实验结果表明，该方法能够高精度检测裂纹。灵敏度为75.71％，特异性为99.9％，精度为68.2％，F值为0.6952。实验结果表明，与基于pix2pix的方法相比，该方法能够以更高的性能检测裂纹。此外，训练时间比XGBoost短7.7倍，比pix2pix短2.3倍。实验结果表明，该方法能够高精度检测裂纹。