Roof bolt identification in underground coal mines from 3D point cloud data using local point descriptors and artificial neural network
Article
Article Title | Roof bolt identification in underground coal mines from 3D point cloud data using local point descriptors and artificial |
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ERA Journal ID | 4642 |
Article Category | Article |
Authors | Singh, Sarvesh Kumar, Raval, Simit and Banerjee, Bikram |
Journal Title | International Journal of Remote Sensing |
Journal Citation | 42 (1), pp. 367-377 |
Number of Pages | 11 |
Year | 2020 |
Publisher | Taylor & Francis |
Place of Publication | United Kingdom |
ISSN | 0143-1161 |
1366-5901 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/2150704x.2020.1809734 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/2150704X.2020.1809734 |
Abstract | Roof bolts are commonly used to provide structural support in underground mines. Frequent and automated assessment of roof bolt is critical to closely monitor any change in the roof conditions while preventing major hazards such as roof fall. However, due to challenging conditions at mine sites such as sub-optimal lighting and restrictive access, it is difficult to routinely assess roof bolts by visual inspection or traditional surveying. To overcome these challenges, this study presents an automated method of roof bolt identification from 3D point cloud data, to assist in spatio-temporal monitoring efforts at mine sites. An artificial neural network was used to classify roof bolts and extract them from 3D point cloud using local point descriptors such as the proportion of variance (POV) over multiple scales, radial surface descriptor (RSD) over multiple scales and fast point feature histogram (FPFH). Accuracy was evaluated in terms ofprecision, recall and quality metric generally used in classification studies. The generated results were compared against other machine learning algorithms such as weighted k-nearest neighbours (k-NN), ensemble subspace k-NN, support vector machine (SVM) and random forest (RF), and was found to be superior by up to 8% in terms of the achieved quality metric. |
Keywords | Bolts; Coal industry; Coal mines; Decision trees; Learning algorithms; Mine roof control; Nearest neighbor search; Roofs; Support vector machines |
ANZSRC Field of Research 2020 | 401306. Surveying (incl. hydrographic surveying) |
401304. Photogrammetry and remote sensing | |
401905. Mining engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of New South Wales |
Agriculture Victoria |
https://research.usq.edu.au/item/z308v/roof-bolt-identification-in-underground-coal-mines-from-3d-point-cloud-data-using-local-point-descriptors-and-artificial-neural-network
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