International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | MineGuard-3D: AI-Powered Mining Safety Monitoring System |
|---|---|
| ABSTRACT | Mining activities occur in highly dynamic and hazardous environments where workers are continuously exposed to structural instabilities, falling debris, equipment collisions, and unauthorized movement within restricted zones. Conventional safety practices—such as manual inspections, scheduled assessments, and 2D video surveillance—lack the capability to capture depth, track geometric changes, or function reliably in low-illumination and dust- heavy underground tunnels. This paper introduces MineGuard-3D, an intelligent, real- time safety monitoring framework that integrates LiDAR, depth cameras, and advanced deep neural networks to analyze three- dimensional spatial data. Field experiments demonstrate that MineGuard-3D achieves more than 95% hazard-detection accuracy and reduces critical near-miss incidents by 67% over six months. The proposed solution represents a significant advancement in predictive safety analytics, offering a scalable, practical, and high-precision approach for modern mining operations. |
| AUTHOR | AISHWARYA G S, KAVERI M, CHANDANA H S, MANDARA R, SAMEER B UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312096 |
| pdf/96_MineGuard-3D AI-Powered Mining Safety Monitoring System.pdf | |
| KEYWORDS | |
| References | [1] O. Gipa, J. Peng, A. Samuel and K. Zhao, “Real- Time Hazard Detection in Mines Using Deep Learning and Computer Vision,” ResearchGate, 2025. [2] X. Wang, T. Huang, L. Chen and M. Fu, “A New Approach for Monitoring Mining Surface 3D Deformation Using UAV-LiDAR Point Cloud Data,” ResearchGate, 2025. [3] B. Cavieres, P. Mellado, R. Lagos, and F. Pizarro, “Dust Filtering in LiDAR Point Clouds Using Deep Learning,” Sensors, vol. 25, no. 20, pp. 1–18, 2025. [4] Y. Zhang, S. Li and C. Sun, “Structural Health Monitoring Based on Three-Dimensional Point Cloud Technology: A Review,” Engineering Reports, Elsevier, 2025. [5] J. Kang, J. Shi, and P. Liu, “A Coal Mine Tunnel Deformation Detection Method Using Handheld 3D Scanner,” Sensors, vol. 24, no. 7, pp. 1–15, 2024. [6] P. Singh, R. Bansal, and S. Kaur, “A Comprehensive Review on the Application of Drone, Virtual Reality and LiDAR for Structural and Environmental Monitoring,” Geomatics, Natural Hazards and Risk, Taylor & Francis, 2024 |
