Research on mining car target recognition method based on machine learning algorithms

Yuwen Fu; Bei Chen; Yanwen Zhang; Haoguang Liu; Yanshi Sun; Zhoujie Wang

doi:10.1117/12.3029618

21 June 2024 Research on mining car target recognition method based on machine learning algorithms

Yuwen Fu, Bei Chen, Yanwen Zhang, Haoguang Liu, Yanshi Sun, Zhoujie Wang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131672X (2024) https://doi.org/10.1117/12.3029618
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In the process of acceptance measurement based on drone point cloud data, the mining cars in the open-pit mining area will generate a large number of pseudo terrain points in the 3D point cloud modeling process. If they are not extracted and removed, the accuracy of acceptance measurement will be seriously reduced. Therefore, how to accurately identify and extract mining car points from 3D point cloud data is the main technical issue for high-precision acceptance of mining sites based on unmanned aerial car oblique photogrammetry technology. Therefore, this article proposes a machine learning algorithm based method for extracting open-pit mining car point sets, which can effectively solve the problem of reduced acceptance accuracy caused by the existence of mining car point sets, thereby improving the accuracy of acceptance measurement. This provides important technical support for open-pit mining site acceptance measurement based on drone photogrammetry technology.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuwen Fu, Bei Chen, Yanwen Zhang, Haoguang Liu, Yanshi Sun, and Zhoujie Wang "Research on mining car target recognition method based on machine learning algorithms", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131672X (21 June 2024); https://doi.org/10.1117/12.3029618

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KEYWORDS

Mining

Point clouds

Machine learning

Education and training

Data modeling

Detection and tracking algorithms

Random forests

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