The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines
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 o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/9/4294 |
| _version_ | 1797601773756612608 |
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| author | Mohamed Imam Karim Baïna Youness Tabii El Mostafa Ressami Youssef Adlaoui Intissar Benzakour El hassan Abdelwahed |
| author_facet | Mohamed Imam Karim Baïna Youness Tabii El Mostafa Ressami Youssef Adlaoui Intissar Benzakour El hassan Abdelwahed |
| author_sort | Mohamed Imam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:07:25Z |
| format | Article |
| id | doaj.art-e9e3bf0bf9934347af7d6950c33756cb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:07:25Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e9e3bf0bf9934347af7d6950c33756cb2023-11-17T23:42:22ZengMDPI AGSensors1424-82202023-04-01239429410.3390/s23094294The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground MinesMohamed Imam0Karim Baïna1Youness Tabii2El Mostafa Ressami3Youssef Adlaoui4Intissar Benzakour5El hassan Abdelwahed6Alqualsadi (Digital Innovation on Enterprise Architectures) Research Team & IRDA (Information Retrieval and Data Analytics) Research Team, Rabat IT Center, ENSIAS, Mohammed V University, Rabat 10112, MoroccoAlqualsadi (Digital Innovation on Enterprise Architectures) Research Team & IRDA (Information Retrieval and Data Analytics) Research Team, Rabat IT Center, ENSIAS, Mohammed V University, Rabat 10112, MoroccoAlqualsadi (Digital Innovation on Enterprise Architectures) Research Team & IRDA (Information Retrieval and Data Analytics) Research Team, Rabat IT Center, ENSIAS, Mohammed V University, Rabat 10112, MoroccoMASciR (Moroccan Foundation for Advanced Science), Innovation and Research, Rabat 10112, MoroccoReminex (Research & Development, Engineering and Project Delivery Arm), Managem, Casablanca 20250, MoroccoReminex (Research & Development, Engineering and Project Delivery Arm), Managem, Casablanca 20250, MoroccoFaculté des Sciences Semlalia de Marrakech (FSSM), Cadi Ayyad University, Marrakech 40000, MoroccoUnderground 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.https://www.mdpi.com/1424-8220/23/9/4294anti-collision systemscollision avoidancepedestrian detectionunderground minescomputer visiondeep learning |
| spellingShingle | Mohamed Imam Karim Baïna Youness Tabii El Mostafa Ressami Youssef Adlaoui Intissar Benzakour El hassan Abdelwahed The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines Sensors anti-collision systems collision avoidance pedestrian detection underground mines computer vision deep learning |
| title | The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines |
| title_full | The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines |
| title_fullStr | The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines |
| title_full_unstemmed | The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines |
| title_short | The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines |
| title_sort | future of mine safety a comprehensive review of anti collision systems based on computer vision in underground mines |
| topic | anti-collision systems collision avoidance pedestrian detection underground mines computer vision deep learning |
| url | https://www.mdpi.com/1424-8220/23/9/4294 |
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