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Article

A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners

1
School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia
2
School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2701; https://doi.org/10.3390/rs17152701
Submission received: 23 June 2025 / Revised: 24 July 2025 / Accepted: 1 August 2025 / Published: 4 August 2025
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))

Abstract

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments.
Keywords: mobile laser scanning; 3D point cloud; deep learning; geometry-sensitive data filtering; semantic segmentation; rock bolts; subterranean support system mobile laser scanning; 3D point cloud; deep learning; geometry-sensitive data filtering; semantic segmentation; rock bolts; subterranean support system

Share and Cite

MDPI and ACS Style

Patra, D.; Ranasinghe, P.; Banerjee, B.; Raval, S. A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners. Remote Sens. 2025, 17, 2701. https://doi.org/10.3390/rs17152701

AMA Style

Patra D, Ranasinghe P, Banerjee B, Raval S. A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners. Remote Sensing. 2025; 17(15):2701. https://doi.org/10.3390/rs17152701

Chicago/Turabian Style

Patra, Dibyayan, Pasindu Ranasinghe, Bikram Banerjee, and Simit Raval. 2025. "A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners" Remote Sensing 17, no. 15: 2701. https://doi.org/10.3390/rs17152701

APA Style

Patra, D., Ranasinghe, P., Banerjee, B., & Raval, S. (2025). A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners. Remote Sensing, 17(15), 2701. https://doi.org/10.3390/rs17152701

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