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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm
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An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

机译：基于改进的OTSU算法的自动混凝土裂缝检测方法定影点云和图像

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摘要

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.
机译：裂缝是混凝土表面上发生的主要苦心之一。用于检测基于二维（2D）图像的裂缝的传统方法可以被污渍，阴影和其他伪像阻碍，而在这方面，使用点云的各种三维（3D）裂纹检测技术较小但是受到3D激光扫描仪的测量精度的限制。在本研究中，我们提出了一种自动裂缝检测方法，该方法基于改进的OTSU算法融合3D点云和2D图像，该算法包括以下四个主要程序。首先，执行从3D点云和2D图像投影的深度图像的高精度配准。其次，执行像素级图像融合，其熔化深度和灰色信息。第三，使用改进的OTSU方法从融合图像获得粗糙裂缝图像。最后，使用连接的域标记和形态学方法来精细提取裂缝。通过实验，在多个尺度和各种类型的混凝土裂缝处测试了所提出的方法。结果表明，该方法可以达到89.0％的平均精度，召回84.8％，F1得分为86.7％，表现明显优于单一图像（平均F1分数为67.6％）和单点云（平均f1得分为76.0％）方法。因此，该方法具有高检测精度和普遍性，表明其宽潜在应用是混凝土裂纹检测的自动方法。

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