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PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites.

ABSTRACT: With numerous countermeasures, the number of deaths in the construction industry is still higher compared to other industries. Personal Protective Equipment (PPE) is constantly being improved to avoid these accidents, although workers intentionally or unintentionally forget to use such safety measures. It is challenging to manually run a safety check as the number of co-workers on a site can be large; however, it is a prime duty of the authority to provide maximum protection to the workers on the working site. From these motivations, we have created a computer vision (CV) based automatic PPE detection system that detects various types of PPE. This study also created a novel dataset named CHVG (four colored hardhats, vest, safety glass) containing eight different classes, including four colored hardhats, vest, safety glass, person body, and person head. The dataset contains 1,699 images and corresponding annotations of these eight classes. For the detection algorithm, this study has used the You Only Look Once (YOLO) family's anchor-free architecture, YOLOX, which yields better performance than the other object detection models within a satisfactory time interval. Moreover, this study found that the YOLOX-m model yields the highest mean average precision (mAP) than the other three versions of the YOLOX.

SUBMITTER: Ferdous M

PROVIDER: S-EPMC9299268 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Modeling the Stability of SARS-CoV-2 on Personal Protective Equipment (PPE).

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Personal Protective Equipment (PPE) in COVID 19 Pandemic: Related Symptoms and Adverse Reactions in Healthcare Workers and General Population.

Project description:ObjectivesTo assess prevalence of Personal Protective Equipment (PPE)-related symptoms and adverse reactions during Coronavirus Disease 2019 pandemics.MethodsWe conducted an observational study among people exposed to various degree of infectious risk. Data were collected with a self-administered online questionnaire.ResultsThe entire cohort complained about a wide range of adverse reactions: respiratory symptoms affected 80.3% of respondents, 68.5% referred pressure-related skin lesions, fewer manifested a dermatosis of different grade or ocular symptoms. Most of the affected individuals belonged to healthcare staff and manifestations were predicted by wearing time (more than 6 h/d). Moreover, symptoms were higher in the healthcare staff wearing N95/FFP2 respirator mask.ConclusionsGiven the crucial role of PPE to contain the pandemic infection, more attention has to be paid to exposed categories, establishing preventive measure of side effects to ensure total safety.

| S-EPMC7864606 | biostudies-literature

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| S-EPMC9675081 | biostudies-literature

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