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The compliance of head-mounted industrial PPE by using deep learning object detectors
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 a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523037/ https://www.ncbi.nlm.nih.gov/pubmed/36175434 http://dx.doi.org/10.1038/s41598-022-20282-9 |
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author | Isailovic, Velibor Peulic, Aleksandar Djapan, Marko Savkovic, Marija Vukicevic, Arso M. |
author_facet | Isailovic, Velibor Peulic, Aleksandar Djapan, Marko Savkovic, Marija Vukicevic, Arso M. |
author_sort | Isailovic, Velibor |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9523037 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95230372022-10-01 The compliance of head-mounted industrial PPE by using deep learning object detectors Isailovic, Velibor Peulic, Aleksandar Djapan, Marko Savkovic, Marija Vukicevic, Arso M. Sci Rep Article 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. Nature Publishing Group UK 2022-09-29 /pmc/articles/PMC9523037/ /pubmed/36175434 http://dx.doi.org/10.1038/s41598-022-20282-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Isailovic, Velibor Peulic, Aleksandar Djapan, Marko Savkovic, Marija Vukicevic, Arso M. The compliance of head-mounted industrial PPE by using deep learning object detectors |
title | The compliance of head-mounted industrial PPE by using deep learning object detectors |
title_full | The compliance of head-mounted industrial PPE by using deep learning object detectors |
title_fullStr | The compliance of head-mounted industrial PPE by using deep learning object detectors |
title_full_unstemmed | The compliance of head-mounted industrial PPE by using deep learning object detectors |
title_short | The compliance of head-mounted industrial PPE by using deep learning object detectors |
title_sort | compliance of head-mounted industrial ppe by using deep learning object detectors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523037/ https://www.ncbi.nlm.nih.gov/pubmed/36175434 http://dx.doi.org/10.1038/s41598-022-20282-9 |
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