Research on mask wearing Detection based on Yolov7

ZeKai Xu

doi:10.1117/12.3011675

7 December 2023 Research on mask wearing Detection based on Yolov7

ZeKai Xu

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413O (2023) https://doi.org/10.1117/12.3011675
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Wearing masks can prevent infection of COVID-19 to a large extent. It is tedious and inefficient for COVID-19 high-risk groups to rely on manual supervision and reminding to wear masks. However, some mask detection systems have the problem of high detection accuracy but low detection rate or high detection rate but low detection accuracy now. As a result, this paper proposes a mask wearing detection model based on YOLOv7 algorithm, which randomly extracts 3600 images from the public RMFRD dataset and uses Mosaic data augmentation technology for experiment. The experimental results show that the detection in the test set achieved an accuracy of 96.43%, a detection speed of 140f/s, and a recall rate of 94.43%. The model achieved good results in both speed and accuracy when performing mask wearing detection tasks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

ZeKai Xu "Research on mask wearing Detection based on Yolov7", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413O (7 December 2023); https://doi.org/10.1117/12.3011675

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KEYWORDS

Object detection

Education and training

Feature extraction

Convolution

Data modeling

Detection and tracking algorithms

Facial recognition systems

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