Real-time detection for mask wearing based on YOLOv5 R6.0 algorithm

Shenghai Yuan; Yudong Wang; Yaxin Zhou; Changze Zhou

doi:10.1117/12.2641819

10 November 2022 Real-time detection for mask wearing based on YOLOv5 R6.0 algorithm

Shenghai Yuan, Yudong Wang, Yaxin Zhou, Changze Zhou

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481M (2022) https://doi.org/10.1117/12.2641819
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

With the advent of large-scale data sets and the widespread usage of convolutional neural networks, deep learning technologies have advanced quickly in recent years, particularly target identification methods in the field of computer vision have become particularly important. This work offers a real-time detection system for mask wearing utilizing the upgraded YOLOv5 R6.0 algorithm in the context of the standardized prevention and control of the new crown epidemic, for the phenomenon that individuals still do not wear masks in public places as necessary. First, we collect the mask wearing dataset from the network, then input it into the YOLOv5 R6.0 model, and finally, the training and testing results are visualized by tensorboard. On the test set, the accuracy (precision), recall (recall), and average precision (mAP) of this algorithm can reach 91%, 97%, and 93.7 percent, respectively, according to the experimental data.

Citation Download Citation

Shenghai Yuan, Yudong Wang, Yaxin Zhou, and Changze Zhou "Real-time detection for mask wearing based on YOLOv5 R6.0 algorithm", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481M (10 November 2022); https://doi.org/10.1117/12.2641819

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