Detection of mask-wearing based on improved YOLOv3

Jiahang Liu; Shenglun Sun; Yixuan Wang

doi:10.1117/12.2659609

30 November 2022 Detection of mask-wearing based on improved YOLOv3

Jiahang Liu, Shenglun Sun, Yixuan Wang

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561V (2022) https://doi.org/10.1117/12.2659609
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

To control the spread of the virus, mask detection is crucial in public areas, especially after the outbreak of Covid-19 pneumonia. This paper aims to improve the accuracy and precision of mask detection. This study improves mask-wearing detection by adding data augmentation, using the smooth label to replace the one-hot vector, and customizing the network connection of the YOLOv3 network. Through these targeted improvements, the average precision of face with mask detection has been increased by 0.9%, and the average precision of face without mask detection has been increased by 2.9%, which implies that it is a better strategy to do mask detection based on YOLOv3. By inputting photographs, the network can check, with high accuracy, whether the pedestrians in the picture wear masks or not, which will be a good supplementary to epidemic prevention and control.

Citation Download Citation

Jiahang Liu, Shenglun Sun, and Yixuan Wang "Detection of mask-wearing based on improved YOLOv3", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561V (30 November 2022); https://doi.org/10.1117/12.2659609

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