Machine learning-based evaluation of the damage using images on concrete infrastructure structures

概要

Identification through image processing has been used in various fields, including product examination. In recent years, this method has been applied to infrastructure inspection, such as identifying cracks in structures. To evaluate the life of a concrete structure, it is necessary to measure the crack width on a concrete surface. Measuring the crack width is an important process when inspecting a concrete surface. The conventional method, in which a human inspector uses a crack gauge or determines the size through a visual evaluation, results in a subjective evaluation of the extent of concrete life. Currently, an operator inspects the concrete structure with a visual examination of the concrete surface. However, this method presents numerous problems, including when the inspector has to work in dangerously high places. Therefore, the automation of infrastructure inspection is required. Image processing can detect cracks in concrete structures using the features of cracks. However, arbitrary features cannot adequately represent the characteristics of cracks, and the detection accuracy is limited. Therefore, we used machine learning to extract the features of cracks. We identified that non- arbitrary features, such as color-related features, are also important. Because concrete is apparently monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in detection. We also compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM). The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false-positive rate was also higher in the proposed method. After obtaining high accuracy in detecting cracks using machine learning, we further attempted to classify these cracks according to their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes based on the different crack widths. In this work, the following two issues are discussed: the first is an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the second is the classification results based on crack width. Finally, we evaluated the degree of damage by classifying the cracks based on crack width using machine learning.