The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines

Abstract

Underground mining operations present critical safety hazards due to limited visibility and blind areas, which can lead to collisions between mobile machines and vehicles or persons, causing accidents and fatalities. This paper aims to survey the existing literature on anti-collision systems based on computer vision for pedestrian detection in underground mines, categorize them based on the types of sensors used, and evaluate their effectiveness in deep underground environments. A systematic review of the literature was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify relevant research work on anti-collision systems for underground mining. The selected studies were analyzed and categorized based on the types of sensors used and their advantages and limitations in deep underground environments. This study provides an overview of the anti-collision systems used in underground mining, including cameras and lidar sensors, and their effectiveness in detecting pedestrians in deep underground environments. Anti-collision systems based on computer vision are effective in reducing accidents and fatalities in underground mining operations. However, their performance is influenced by factors, such as lighting conditions, sensor placement, and sensor range. The findings of this study have significant implications for the mining industry and could help improve safety in underground mining operations. This review and analysis of existing anti-collision systems can guide mining companies in selecting the most suitable system for their specific needs, ultimately reducing the risk of accidents and fatalities.

Detection of Underground Dangerous Area Based on Improving YOLOV8

“…This may result in a delayed response to the occurrence of danger or erroneous judgments of certain behaviors. Therefore, the automated detection of underground personnel in coal mines is essential [3].…”

Forecasting law enforcement frequency of internet+ coal mine safety supervision

“…It refers to the integration of information technologies such as the Internet of Things (IoT), big data, cloud computing and artificial intelligence into the CMSS (Xu, 2014;Sun, 2015). By collecting and analyzing the data from various sources such as sensors, cameras and other devices, potential safety hazards can be detected early on and addressed before they become more serious (Imam et al, 2023). Besides, the use of advanced technologies such as artificial intelligence, machine learning and process automation can help to identify the patterns and trends in the data of hazards, enabling more accurate predictions of potential safety risks (Ali and Frimpong, 2020).…”