A Robust Real-time Component for Personal Protective Equipment Detection in an Industrial Setting

Pedro Torres, André Davys, Thuener Silva, Luiz Schirmer, André Kuramoto, Bruno Itagyba, Cristiane Salgado, Sidney Comandulli, Patricia Ventura, Leonardo Fialho, Marinho Fischer, Marcos Kalinowski, Simone Barbosa, Hélio Lopes

Abstract

In large industries, such as construction, metallurgy, and oil, workers are continually exposed to various hazards in their workplace. Accordingly to the International Labor Organization (ILO), there are 340 million occupational accidents annually. Personal Protective Equipment (PPE) is used to ensure the essential protection of workers’ health and safety. There is a great effort to ensure that these types of equipment are used properly. In such an environment, it is common to have closed-circuit television (CCTV) cameras to monitor workers, as those can be used to verify the PPE’s proper usage. Some works address this problem using CCTV images; however, they frequently can not deal with multiples safe equipment usage detection and others even skip the verification phase, making only the detection. In this paper, we propose a novel cognitive safety analysis component for a monitoring system. This component acts to detect the proper usage of PPE’s in real-time using data stream from regular CCTV cameras. We built the system component based on the top of state-of-art deep learning techniques for object detection. The methodology is robust with consistent and promising results for Mean Average Precision (80.19% mAP) and can act in real-time (80 FPS).

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Paper Citation


in Harvard Style

Torres P., Davys A., Silva T., Schirmer L., Kuramoto A., Itagyba B., Salgado C., Comandulli S., Ventura P., Fialho L., Fischer M., Kalinowski M., Barbosa S. and Lopes H. (2021). A Robust Real-time Component for Personal Protective Equipment Detection in an Industrial Setting. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 693-700. DOI: 10.5220/0010452606930700


in Bibtex Style

@conference{iceis21,
author={Pedro Torres and André Davys and Thuener Silva and Luiz Schirmer and André Kuramoto and Bruno Itagyba and Cristiane Salgado and Sidney Comandulli and Patricia Ventura and Leonardo Fialho and Marinho Fischer and Marcos Kalinowski and Simone Barbosa and Hélio Lopes},
title={A Robust Real-time Component for Personal Protective Equipment Detection in an Industrial Setting},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={693-700},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010452606930700},
isbn={978-989-758-509-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Robust Real-time Component for Personal Protective Equipment Detection in an Industrial Setting
SN - 978-989-758-509-8
AU - Torres P.
AU - Davys A.
AU - Silva T.
AU - Schirmer L.
AU - Kuramoto A.
AU - Itagyba B.
AU - Salgado C.
AU - Comandulli S.
AU - Ventura P.
AU - Fialho L.
AU - Fischer M.
AU - Kalinowski M.
AU - Barbosa S.
AU - Lopes H.
PY - 2021
SP - 693
EP - 700
DO - 10.5220/0010452606930700