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Authors: Bruno Georgevich Ferreira ; Bruno Georgevich Lima and Tiago Figueiredo Vieira

Affiliation: Institute of Computing, Federal University of Alagoas, Maceió, Alagoas, Brazil

Keyword(s): Deep Learning, Object Detection, Visual Inspection, Collective Protection Equipment.

Abstract: Even though Deep Learning models are presenting increasing popularity in a variety of scenarios, there are many demands to which they can be specifically tuned to. We present a real-time, embedded system capable of performing the visual inspection of Collective Protection Equipment conditions such as fire extinguishers (presence of rust or disconnected hose), emergency lamp (disconnected energy cable) and horizontal and vertical signalization, among others. This demand was raised by a glass-manufacturing company which provides devices for optical-fiber solutions. To tackle this specific necessity, we collected and annotated a database with hundreds of in-factory images and assessed three different Deep Learning models aiming at evaluating the trade-off between performance and processing time. A real-world application was developed with potential to reduce time and costs of periodic inspections of the company’s security installations.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ferreira, B.; Lima, B. and Vieira, T. (2020). Visual Inspection of Collective Protection Equipment Conditions with Mobile Deep Learning Models. In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-441-1, SciTePress, pages 76-83. DOI: 10.5220/0009834600760083

@conference{delta20,
author={Bruno Georgevich Ferreira. and Bruno Georgevich Lima. and Tiago Figueiredo Vieira.},
title={Visual Inspection of Collective Protection Equipment Conditions with Mobile Deep Learning Models},
booktitle={Proceedings of the 1st International Conference on Deep Learning Theory and Applications - DeLTA},
year={2020},
pages={76-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009834600760083},
isbn={978-989-758-441-1},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - DeLTA
TI - Visual Inspection of Collective Protection Equipment Conditions with Mobile Deep Learning Models
SN - 978-989-758-441-1
AU - Ferreira, B.
AU - Lima, B.
AU - Vieira, T.
PY - 2020
SP - 76
EP - 83
DO - 10.5220/0009834600760083
PB - SciTePress