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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 9; p. 4294
• Main Authors: Imam, Mohamed, Baïna, Karim, Tabii, Youness, Ressami, El Mostafa, Adlaoui, Youssef, Benzakour, Intissar, Abdelwahed, El hassan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.04.2023; MDPI
• Subjects: Accident prevention; anti-collision systems; Artificial intelligence; collision avoidance; Computer vision; deep learning; Failure; Fatalities; Machine vision; Machinery; Mine accidents; Mine safety; Mineral industry; Mines; Mining accidents & safety; Mining industry; pedestrian detection; Review; Safety and security measures; Sensors; Strip mining; Traffic accidents & safety; Trucks; underground mines; Vehicles; Morocco; Ghana; collision avoidance; deep learning; underground mines; pedestrian detection; computer vision; anti-collision systems; artificial intelligence

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...