A model for face mask detection through deep learning

Ruihang Xu; Xuanjing Li; Xing Tian

doi:10.1117/12.3004703

16 August 2023 A model for face mask detection through deep learning

Ruihang Xu, Xuanjing Li, Xing Tian

Author Affiliations +

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871M (2023) https://doi.org/10.1117/12.3004703
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Since the outbreak of the new coronavirus in China in 2019, wearing masks has gained widespread attention to prevent virus transmission. However, traditional face detection models have struggled to accurately detect faces covered by masks, posing a challenge for public health and security applications. In this study, we propose a novel lightweight model for face-mask detection, called YOLO-ARGhost, which is based on YOLOv4 and incorporates an attention mechanism to enhance accuracy. Our model is designed for fast face-mask detection, overcoming the limitations of previous models. Experimental evaluations on the AIZOO dataset demonstrate that our approach achieves an impressive mean average precision (mAP) of 92.8%.

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

Citation Download Citation

Ruihang Xu, Xuanjing Li, and Xing Tian "A model for face mask detection through deep learning", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871M (16 August 2023); https://doi.org/10.1117/12.3004703

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KEYWORDS

Object detection

Facial recognition systems

Feature extraction

Deep learning

Data modeling

Image processing

Performance modeling

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