Next Article in Journal
Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring
Previous Article in Journal
Combined Study of a Significant Mine Collapse Based on Seismological and Geodetic Data—29 January 2019, Rudna Mine, Poland
Open AccessArticle

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

1
Institute of Land Reclamation and Ecological Restoration, China University of Mining and Technology, Beijing 100083, China
2
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3
Yulin Economic Development Zone, Yulin 719000, China
4
Shenmu Hanjiawan Coal Mining Company Ltd., Shanxi Coal and Chemical Industry Group, Shenmu 719315, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1571; https://doi.org/10.3390/rs12101571
Received: 28 March 2020 / Revised: 12 May 2020 / Accepted: 14 May 2020 / Published: 15 May 2020
(This article belongs to the Section Environmental Remote Sensing)
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. View Full-Text
Keywords: crack classification; UAV images; machine learning crack classification; UAV images; machine learning
Show Figures

Graphical abstract

MDPI and ACS Style

Zhang, F.; Hu, Z.; Fu, Y.; Yang, K.; Wu, Q.; Feng, Z. A New Identification Method for Surface Cracks from UAV Images Based on Machine Learning in Coal Mining Areas. Remote Sens. 2020, 12, 1571. https://doi.org/10.3390/rs12101571

AMA Style

Zhang F, Hu Z, Fu Y, Yang K, Wu Q, Feng Z. A New Identification Method for Surface Cracks from UAV Images Based on Machine Learning in Coal Mining Areas. Remote Sensing. 2020; 12(10):1571. https://doi.org/10.3390/rs12101571

Chicago/Turabian Style

Zhang, Fan; Hu, Zhenqi; Fu, Yaokun; Yang, Kun; Wu, Qunying; Feng, Zewei. 2020. "A New Identification Method for Surface Cracks from UAV Images Based on Machine Learning in Coal Mining Areas" Remote Sens. 12, no. 10: 1571. https://doi.org/10.3390/rs12101571

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop