A New Identification Method for Surface Cracks from UAV Images Based on Machine Learning in Coal Mining Areas

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-05, Vol.12 (10), p.1571
• Main Authors: Zhang, Fan, Hu, Zhenqi, Fu, Yaokun, Yang, Kun, Wu, Qunying, Feng, Zewei
• Format: Article
• Language: English
• Subjects: Accuracy; Airborne radar; Algorithms; Classification; Coal mines; Coal mining; Comparative analysis; crack classification; Cracks; Datasets; Deep learning; Efficiency; Geology; Image acquisition; Image enhancement; Image processing; Learning algorithms; Machine learning; Methods; Penalty function; Principal components analysis; Remote sensing; Research methodology; Sharpening; Support vector machines; Surface cracks; UAV images; Unmanned aerial vehicles; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12101571

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.