MaskHunter: real‐time object detection of face masks during the COVID‐19 pandemic. Issue 16 (23rd February 2021)

• Record Type: Journal Article
• Authors: Cao, Zhihao; Shao, Mingfeng; Xu, Li; Mu, Shaomin; Qu, Hongchun
• Is Part Of: IET image processing. Volume 14:Issue 16(2020)
• Page Start: 4359
• Page End: 4367
• Publication Date: 2021-02-23
• Subjects: object detection -- pedestrians; MaskHunter -- real‐time object detection -- COVID‐19 pandemic -- public places -- real‐time mask detection -- face mask detection effect -- night environment -- novel mask‐guided module -- detection performance -- novel improved Mosaic data augmentation method; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.1119
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Due to the COVID‐19 pandemic at present, it is necessary to detect whether pedestrians in public places wear face masks or not for preventing the spread of novel coronavirus. The pedestrian flow in public places is large, and it puts forward higher requirements for the accuracy and speed of real‐time mask detection. Improving the face mask detection effect especially in the night environment is a challenging problem. A novel object detector namely MaskHunter is proposed in this study for the real‐time mask detection. Specifically, the authors propose novel effective structures of backbone, neck and prediction head based on YOLOv4 series, which achieves the state‐of‐the‐art performance and a novel improved Mosaic data augmentation method. Moreover, they propose a novel mask‐guided module to enhance the discrimination ability of face mask especially in the night environment. As a consequent, experiments show that MaskHunter achieves better detection performance for real‐time mask detection compared with other obtained models in this scenario.