Safety Warning of Mine Conveyor Belt Based on Binocular Vision
Open Access
- 15 October 2022
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 14 (20) , 13276
Abstract
For the wear and damage to mine conveyor belts by foreign materials, such as large materials, this paper designs a volume measurement method for large materials based on binocular vision by the volume measurement of the large material inon the conveyor belt, provides safety-warning data of over-volume limits for mine conveyor belts, and prevents damage to the mine conveyor belts. In this design, first, by obtaining the binocular camera parameters to improve the image-edge information, images taken with a binocular camera were then aligned by the Bouguet stereo correction algorithm. Finally, the disparity map was calculated by the semi-global-block matching (SGBM) stereo matching, and to get the target 3D coordinates, volume measurements were performed by the micrometric method. The study shows that the experimental error remains at about 5.65%, which provides a low-cost measurement method for raw-coal-volume measurement of the well industry and coal-mine belt conveyor.Keywords
Funding Information
- National Natural Science Foundation of China (52174137, 51704275)
- China Postdoctoral Science Foundation (2020T130697, 2019M661994)
- Shanxi Datong University Youth Scientific Research Fund Project (2020Q5)
- Shanxi Postgraduate Education Innovation Project (2021Y739, 2022Y766)
- Shanxi Datong University 2021 Industry-University-Research Project (2021CXZ2)
- Shanxi Datong University 2022 Campus level Opening Bidding Project (2021ZBZX3)
- Shanxi Datong University Graduate Education Innovation Project (21CX02, 22CX07, 22CX34, 22CX42, 22CX44)
This publication has 4 references indexed in Scilit:
- Investigating the anchorage performance of full-grouted anchor bolts with a modified numerical simulation methodEngineering Failure Analysis, 2022
- Anchorage performance of a modified cable anchor subjected to different joint opening conditionsConstruction and Building Materials, 2022
- Learning to detect natural image boundaries using local brightness, color, and texture cuesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2004
- A flexible new technique for camera calibrationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000