Type de document
Études primaires
Année de publication
2026
Langue
Anglais
Titre de la revue
Applied Sciences
Résumé
Computer vision (CV) technologies have emerged as promising tools for real-time collision risk management. This paper examines CV-based collision-prevention systems from academic and industrial perspectives with a focus on identifying technical and implementation challenges affecting their deployment on construction sites. A scoping review of 158 publications was conducted, alongside an assessment of commercial CV-based collision-prevention systems currently used in construction. The analysis focused on five key functions: object detection, distance measurement and proximity estimation, tracking, worker re-identification (ReID), and deployment robustness. Academic studies demonstrate substantial progress in detection accuracy, real-time processing, trajectory tracking, and predictive risk assessment through deep learning approaches. However, performance remains sensitive to occlusions, lighting variations, environmental conditions, and site-specific characteristics. The commercial systems assessed in this study, while generally designed for reliability and ease of integration, often rely on simplified zone-based warning logic and provide limited transparency regarding performance validation, tracking capabilities, and distance estimation accuracy. The review highlights a persistent gap between laboratory performance and operational effectiveness on construction sites. The findings point to the need for greater robustness under real-world conditions, validated metric-distance accuracy, improved temporal continuity through tracking and ReID, and standardized evaluation protocols incorporating safety-relevant performance metrics. As AI-enabled systems increasingly support safety-related functions, their development should also align with established machine-safety principles and emerging standards. ©MDPI
Mots-clés
Santé et sécurité du travail, Occupational health and safety, Chantier de construction, Construction site, Piéton, Pedestrian, Collision, Intelligence artificielle, Artificial intelligence, Détecteur de proximité, Proximity detector guard
Numéro de projet IRSST
2023-0008
Citation recommandée
Ziadia, M., Gauthier, F. et Burlet-Vienney, D. (2026). Computer vision-based collision prevention in construction: Bridging academic advances and industrial requirements. Applied Sciences, 16(21) article 10507. https://doi.org/10.3390/app162110507