Real-Time AI-Driven Hazard Detection: Integrating Computer Vision and Sensor Networks for Enhanced Mining Safety

Abstract

This article presents a comprehensive analysis of real-time hazard detection systems in mining operations through the integration of computer vision and sensor networks. The article explores how artificial intelligence and advanced monitoring technologies are transforming traditional mining safety protocols, introducing innovative solutions for early hazard detection and emergency response. The article examines the implementation of sophisticated model architectures for video analytics, multilayered sensor networks, and data integration frameworks that enable precise tracking of worker behavior, equipment proximity, and environmental conditions. Through detailed investigation of system performance metrics, implementation challenges, and validation processes, this article demonstrates the significant impact of AI-driven safety systems on reducing workplace incidents and improving operational efficiency. The article also addresses critical challenges in underground mining environments, including environmental factors, technical constraints, and data quality management, while providing insights into future developments and best practices for industry adoption. This comprehensive approach to mining safety represents a significant advancement in protecting worker safety while maintaining productive operations.