Industrial Safety Helmet Detection Using Single Shot Detectors Models and Transfer Learning

DOI

10.2991/978-94-6463-082-4_34How to use a DOI?

Keywords

SSD Models; SSD Mobilenet V2; SSD Resnet50; Object detection; Safety helmet detection; Transfer learning

Abstract

Personal safety is concerned to be as a crucial part for the industrial workers while working in an industrial environment. Industries provide personal protective equipment to their workers to ensure their safety, similarly the workers are also meant to wear and follow all the regulations regarding the personal protective equipment’s (PPEs) provided to them. Our study provides the methodology to detect the industrial safety helmet using the surveillance cameras. In this study, we have trained two different single shot detector models i.e., Single Shot Detector (SSD) MobilenetV2 and Single Shot Detector (SSD) Resnet50 and used transfer learning methodology to detect the industrial safety helmet. We have utilized a publically available dataset from Kaggle website and utilized that dataset for the purpose of training the models. Furthermore, the models evaluation is done based on these parameters i.e., classification loss, localization loss, regularization loss and total loss. However, we concluded that the SSD Mobilenet V2 performs better than SSD Resnet50 model based on loss parameters. For SSD Mobilenet v2 we achieved a classification loss of 0.11, localization loss of 0.05, regularization loss of 0.15, and a total loss as 0.32 respectively. Moreover, the graphs for the loss of each model has also been studied.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

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TY  - CONF
AU  - Muhammad Umair
AU  - Yee-Loo Foo
PY  - 2022
DA  - 2022/12/23
TI  - Industrial Safety Helmet Detection Using Single Shot Detectors Models and Transfer Learning
BT  - Proceedings of the Multimedia University Engineering Conference (MECON 2022)
PB  - Atlantis Press
SP  - 390
EP  - 400
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-082-4_34
DO  - 10.2991/978-94-6463-082-4_34
ID  - Umair2022
ER  -