Systematic review of computer-vision technologies for personal protective equipment compliance monitoring
Artykuł przeglądowy (review)
MNiSW
20
Lista 2024
| Status: | |
| Autorzy: | Barlybayev Alibek, Miłosz Marek, Amangeldy Nurzada, Li Guohui, Razakhova Bibigul, Tazhibay Aruzhan, Nazyrova Aizhan, Lamasheva Zhanar |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 6 |
| Wolumen/Tom: | 15 |
| Numer artykułu: | 388 |
| Strony: | 1 - 48 |
| Impact Factor: | 5,2 |
| Web of Science® Times Cited: | 0 |
| Bazy: | Web of Science |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23483557) |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 16 czerwca 2026 |
| Abstrakty: | angielski |
| This systematic review investigates the application of computer-vision technologies for automated monitoring of personal protective equipment compliance in industrial environments. This review followed the PRISMA 2020 guidelines and covered studies published between 2010 and 24 February 2026. It provides a structured synthesis of advances in deep learning-based object detection models, with particular emphasis on different YOLO variants, two-stage detectors such as Faster R-CNN, and emerging transformer-based and vision–language models. Model effectiveness, reported performance metrics, and dataset characteristics are comparatively examined, including their performance under practical operating conditions. Special attention is given to performance variability in real-world scenarios affected by illumination changes, occlusion, viewing angle variation, worker movement, computational constraints, and large-scale deployment requirements. The review also appraises the reporting quality and risk of bias of the included studies and identifies current research trends, methodological limitations, and the gap between laboratory validation and industrial implementation. It also outlines future directions for improving the reliability, cost-effectiveness, and practical application of computer vision-based personal protective equipment compliance systems. |
