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

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.

Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories

“…Empirical evidence suggests varied strengths among these algorithms: YOLOv6 often surpasses its predecessors in general performance, largely owing to its more advanced development. However, this does not diminish the notable precision and high mean Average Precision (mAP) score of YOLOv4 [23], nor the exceptional detection speed of YOLOv5 [24]. Conversely, YOLOv6, while more accurate overall [20], has shown limitations in close-up object detection and stability compared to YOLOv5 [20].…”