Author:
Jiang Xiaoming,Xiang Fugui,Lv Minghong,Wang Wei,Zhang Zhonghua,Yu Yi
Abstract
Abstract
The use of face mask is advised by World Health Organization (WHO) for preventing transmission of Coronavirus disease 2019 (COVID-19). It is of great value to solve the multi-task object detection problem of non-wearing mask, wrong way wearing mask and standard wearing mask. In this paper, a network YOLOv3_Slim based on YOLOv3 is implemented. It’s faster than YOLOv3. Detection speed increased from 15.67 fps to 16.89 fps. In the mean time, we found the effect of the difference of inner class on the classification ability of the model. The large error of inner class will reduce the accuracy of the model and make the attention mechanism ineffective. So after changing the labels of the third data set, We add ECA module to our network. YOLOv3_Slim is more accurate than YOLOv4 in face mask recognition based on our data set. The mAP increased from 89.45% to 92.50%.
Subject
General Physics and Astronomy
Cited by
5 articles.
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1. Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic;Systems;2023-02-17
2. An Edge-based Real-Time Object Detection;2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA);2022-12
3. Comparative Analysis of Object Detection Models for the Detection of Multiple Face Masks;International Conference on Innovative Computing and Communications;2022-11-08
4. Facial mask detection system based on YOLOv4 algorithm;2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA);2022-06-24
5. Face-Mask Recognition and Detection Using Deep Learning;2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON);2022-05-26