The compliance of head-mounted industrial PPE by using deep learning object detectors
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The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author’s collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)—which in literature were studied only separ...ately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types—while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity.
Извор:
Scientific Reports, 2022, 12, 16347-Издавач:
- Nature Research
Финансирање / пројекти:
- AI4WorkplaceSafety - Artificial Intelligence for Managing Workplace Safety (RS-ScienceFundRS-AI-6524219)
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Geografski fakultetTY - JOUR AU - Isailović, Velibor AU - Peulić, Aleksandar AU - Đapan, Marko AU - Savković, Marija AU - Vukićević, Arso M. PY - 2022 UR - http://gery.gef.bg.ac.rs/handle/123456789/1218 AB - The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author’s collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)—which in literature were studied only separately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types—while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity. PB - Nature Research T2 - Scientific Reports T1 - The compliance of head-mounted industrial PPE by using deep learning object detectors VL - 12 SP - 16347 DO - 10.1038/s41598-022-20282-9 ER -
@article{ author = "Isailović, Velibor and Peulić, Aleksandar and Đapan, Marko and Savković, Marija and Vukićević, Arso M.", year = "2022", abstract = "The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author’s collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)—which in literature were studied only separately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types—while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity.", publisher = "Nature Research", journal = "Scientific Reports", title = "The compliance of head-mounted industrial PPE by using deep learning object detectors", volume = "12", pages = "16347", doi = "10.1038/s41598-022-20282-9" }
Isailović, V., Peulić, A., Đapan, M., Savković, M.,& Vukićević, A. M.. (2022). The compliance of head-mounted industrial PPE by using deep learning object detectors. in Scientific Reports Nature Research., 12, 16347. https://doi.org/10.1038/s41598-022-20282-9
Isailović V, Peulić A, Đapan M, Savković M, Vukićević AM. The compliance of head-mounted industrial PPE by using deep learning object detectors. in Scientific Reports. 2022;12:16347. doi:10.1038/s41598-022-20282-9 .
Isailović, Velibor, Peulić, Aleksandar, Đapan, Marko, Savković, Marija, Vukićević, Arso M., "The compliance of head-mounted industrial PPE by using deep learning object detectors" in Scientific Reports, 12 (2022):16347, https://doi.org/10.1038/s41598-022-20282-9 . .