Anti-Collision System for Accident Prevention in Underground Mines using Computer Vision

Imam, Mohamed; Bäına, Karim; Tabii, Youness; Benzakour, Intissar; Adlaoui, Youssef; Ressami, El Mostafa; Abdelwahed, El Hassan

doi:10.1145/3571560.3571574

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…Imam et al [93] propose a new anti-collision system for pedestrian detection in underground mines based on RGB images collected in five different mines. They used Yolov5s which reaches 75% of precision and 71% of MAP.…”

Section: Mobile Machinesmentioning

confidence: 99%

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

Imam

Bäına

Tabii

et al. 2023

Sensors

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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.

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Section: Mobile Machinesmentioning

confidence: 99%

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

Imam

Bäına

Tabii

et al. 2023

Sensors

Self Cite

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“…The system uses a custom point cloud clustering algorithm designed for challenging mine environments to extract obstacle information and then uses the YOLOv5 algorithm to identify obstacles in the generated images. Imam et al [20] proposed a new underground pedestrian detection and anti-collision system based on RGB images collected from five different mines. The accuracy of the yolov5 they used reached 75%, and the MAP reached 71%.…”

Section: Introductionmentioning

confidence: 99%

Detection of Underground Dangerous Area Based on Improving YOLOV8

Ni,

Huo,

Hou

et al. 2024

Electronics

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In order to improve the safety needs of personnel in the dark environment under the well, this article adopts the improved YOLOV8 algorithm combined with the ray method to determine whether underground personnel are entering dangerous areas and to provide early warning. First of all, this article introduces the coordinate attention mechanism on the basis of YOLOV8 target detection so that the model pays attention to the location information of the target area so as to improve the detection accuracy of obstruction and small target areas. In addition, the Soft-Non-Maximum Suppression (SNMS) module is introduced to further improve accuracy. The improved model is then combined with the ray method to be deployed and applied under a variety of angles and different scenic information cameras. The experimental results show that the proposed method obtains 99.5% of the identification accuracy and a frame speed of 45 Frames Per Second (FPS) on the self-built dataset. Compared with the YOLOV8 model, it has a higher accuracy and can effectively cope with the changes and interference factors in the underground environment. Further, it meets the requirements for real-time testing in dangerous underground areas.

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Innovative predictive maintenance for mining grinding mills: from LSTM-based vibration forecasting to pixel-based MFCC image and CNN

Rihi,

Baïna,

Mhada

et al. 2024

Int J Adv Manuf Technol

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Anti-Collision System for Accident Prevention in Underground Mines using Computer Vision

Cited by 5 publications

References 1 publication

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

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

Detection of Underground Dangerous Area Based on Improving YOLOV8

Innovative predictive maintenance for mining grinding mills: from LSTM-based vibration forecasting to pixel-based MFCC image and CNN

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