Abstract
Obtaining real-time, objective, and high-precision distribution information of surface cracks in mining areas is the first task for studying the development regularity of surface cracks and evaluating the risk. The complex geological environment in the mining area leads to low accuracy and efficiency of the existing extracting cracks methods from unmanned air vehicle (UAV) images. Therefore, this manuscript proposes a new identification method of surface cracks from UAV images based on machine learning in coal mining areas. First, the acquired UAV image is cut into small sub-images, and divided into four datasets according to the characteristics of background information: Bright Ground, Dark Dround, Withered Vegetation, and Green Vegetation. Then, for each dataset, a training sample is established with cracks and no cracks as labels and the RGB (red, green, and blue) three-band value of the sub-image as feature. Finally, the best machine learning algorithms, dimensionality reduction methods and image processing techniques are obtained through comparative analysis. The results show that using the V-SVM (Support vector machine with V as penalty function) machine learning algorithm, principal component analysis (PCA) to reduce the full features to 95% of the original variance, and image color enhancement by Laplace sharpening, the overall accuracy could reach 88.99%. This proves that the method proposed in this manuscript can achieve high-precision crack extraction from UAV image.
Highlights
In western China, especially in the sandy areas, surface cracks are one of the geological environmental problems caused by coal mining [1]
unmanned air vehicle (UAV) remote sensing technology plays an important role in land reclamation in mining areas and has the characteristics of low cost and high efficiency
This article proposes a new identification method for surface cracks from UAV images based on machine learning in coal mining areas
Summary
In western China, especially in the sandy areas, surface cracks are one of the geological environmental problems caused by coal mining [1]. Surface cracks have caused deformation of buildings, damage to underground pipelines, damage to cultivated land, accelerated soil moisture evaporation, vegetation destruction, and soil erosion [2,3,4] This creates considerable difficulties for mining area management staff. Traditional surface crack information acquisition methods mainly include field surveys, radar detection technology [6,7], and satellite remote sensing images [8]. Unmanned air vehicles (UAVs) have significant advantages, such as high resolution, flexible maneuverability, high efficiency, and low operating costs [12] Their resolution can reach the level of centimeters [13], which provides an ideal data source for information extraction of surface cracks in mining areas. Final classification result by the method proposed in this article
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