Open Access

ARTICLE

Detection of Worker’s Safety Helmet and Mask and Identification of Worker Using Deeplearning

by NaeJoung Kwak¹, DongJu Kim^2,*

1 Department of Information Security at Paichai University, Daejeon, 35345, Korea
2 POSTECH Institute of Artificial Intelligence, Pohang, 24257, Korea

* Corresponding Author: DongJu Kim. Email:

Computers, Materials & Continua 2023, 75(1), 1671-1686. https://doi.org/10.32604/cmc.2023.035762

Received 02 September 2022; Accepted 14 December 2022; Issue published 06 February 2023

Abstract

This paper proposes a method for detecting a helmet for the safety of workers from risk factors and a mask worn indoors and verifying a worker’s identity while wearing a helmet and mask for security. The proposed method consists of a part for detecting the worker’s helmet and mask and a part for verifying the worker’s identity. An algorithm for helmet and mask detection is generated by transfer learning of Yolov5’s s-model and m-model. Both models are trained by changing the learning rate, batch size, and epoch. The model with the best performance is selected as the model for detecting masks and helmets. At a learning rate of 0.001, a batch size of 32, and an epoch of 200, the s-model showed the best performance with a mAP of 0.954, and this was selected as an optimal model. The worker’s identification algorithm consists of a facial feature extraction part and a classifier part for the worker’s identification. The algorithm for facial feature extraction is generated by transfer learning of Facenet, and SVM is used as the classifier for identification. The proposed method makes trained models using two datasets, a masked face dataset with only a masked face, and a mixed face dataset with both a masked face and an unmasked face. And the model with the best performance among the trained models was selected as the optimal model for identification when using a mask. As a result of the experiment, the model by transfer learning of Facenet and SVM using a mixed face dataset showed the best performance. When the optimal model was tested with a mixed dataset, it showed an accuracy of 95.4%. Also, the proposed model was evaluated as data from 500 images of taking 10 people with a mobile phone. The results showed that the helmet and mask were detected well and identification was also good.

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Keywords

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