Abstract
In order to effectively control the damage caused by surface cracks to a geological environment, we need to find a convenient, efficient, and accurate method to obtain crack information. The existing crack extraction methods based on unmanned air vehicle (UAV) images inevitably have some erroneous pixels because of the complexity of background information. At the same time, there are few researches on crack feature information. In view of this, this article proposes a surface crack extraction method based on machine learning of UAV images, the data preprocessing steps, and the content and calculation methods for crack feature information: length, width, direction, location, fractal dimension, number, crack rate, and dispersion rate. The results show that the method in this article can effectively avoid the interference by vegetation and soil crust. By introducing the concept of dispersion rate, the method combining crack rate and dispersion rate can describe the distribution characteristics of regional cracks more clearly. Compared to field survey data, the calculation result of the crack feature information in this article is close to the true value, which proves that this is a reliable method for obtaining quantitative crack feature information.
Highlights
In western China, especially in arid and semiarid areas, surface cracks are caused by human activities, such as coal mining [1], soil erosion, building damage, vegetation withering, and other geological environment problems caused by surface cracks [2,3,4]
This article proposes a method for crack extraction of unmanned air vehicle (UAV) images based on machine learning
The crack extraction image obtained above is performed by skeleton extract removal, and intersection-point processing, as shown in Figure 16c, which prov support for subsequent quantitative acquisition of crack feature information
Summary
In western China, especially in arid and semiarid areas, surface cracks are caused by human activities, such as coal mining [1], soil erosion, building damage, vegetation withering, and other geological environment problems caused by surface cracks [2,3,4]. The methods of crack extraction mainly include field surveys, radar detection technology [6,7], satellite remote sensing imaging [8], and unmanned air vehicle (UAV) imaging [9]. Due to the complex geological environment in arid and semiarid areas, the ground surface has large fluctuations and the distribution of soil crusts and vegetation is disorderly. Satellite remote sensing imaging is a method used to extract cracks from images, but the accuracy is low. 0.9431 classification accuracy of the bare land dataset reached 87.75%, the classification accuracy of Green Vegetation
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