MaskHunter: real‐time object detection of face masks during the COVID‐19 pandemic

Cao, Zhihao; Shao, Mingfeng; Xu, Li; Mu, Shaomin; Qu, Hongchun

doi:10.1049/iet-ipr.2020.1119

Cited by 29 publications

(17 citation statements)

References 46 publications

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“…The evolution from GIoU to CIoU makes the regression loss more accurate and the target frame regression more stable. However, it is found that the above three types of loss functions will cause the problem of an inaccurate positioning frame for targets with a high aspect ratio and dense targets; in order to solve this problem, the pixel IoU (PIoU) function [27] is introduced.…”

Section: Loss Function Improvedmentioning

confidence: 99%

HRM: An Intelligent Helmet Recognition Model in Complex Scenes

Yared

et al. 2022

Advances in Mathematical Physics

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This paper presents an intelligent helmet recognition model in complex scenes based on YOLOv5. Firstly, in construction site projects, consider that the photograph which needs to be identified has numerous problems. For example, helmet’s pixels are too tiny to detect, or a large number of workers makes helmets appear densely. A SE-Net channel attention module is added in different parts of the network layer of the model, so that the improved model can pay more attention to the global variables and increase the detection performance of small target information and dense target information. In addition, this paper constructs a helmet data set based on projects and adds training samples of dense targets and long-range small targets. Finally, the modified mosaic data enhancement reduces the influence of redundant background on the model and improves the recognition accuracy of the tiny target. The experimental results show that in the project, the average accuracy of helmet detection reaches 92.82%. Compared with SSD, YOLOv3, and YOLOv5, the average accuracy of this algorithm is improved by 6.89%, 8.28%, and 2.44% and has strong generalization ability in dense scenes and small target scenes, which meets the accuracy requirements of helmet wearing detection in engineering applications.

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Section: Loss Function Improvedmentioning

confidence: 99%

HRM: An Intelligent Helmet Recognition Model in Complex Scenes

Yared

et al. 2022

Advances in Mathematical Physics

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“…While the previously mentioned studies tackle the face mask recognition problem as a classification task, object detection based approaches utilize You Only Look Once (YOLO) approaches also report 94% and 81% average precision [3,28].…”

Section: Face Mask Recognitionmentioning

confidence: 99%

Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning

Seneviratne,

Kasthuriaarachchi,

Rasnayaka

2021

Preprint

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The COVID-19 pandemic has drastically changed accepted norms globally. Within the past year, masks have been used as a public health response to limit the spread of the virus. This sudden change has rendered many face recognition based access control, authentication and surveillance systems ineffective. Official documents such as passports, driving license and national identity cards are enrolled with fully uncovered face images. However, in the current global situation, face matching systems should be able to match these reference images with masked face images. As an example, in an airport or security checkpoint it is safer to match the unmasked image of the identifying document to the masked person rather than asking them to remove the mask. We find that current facial recognition techniques are not robust to this form of occlusion. To address this unique requirement presented due to the current circumstance, we propose a set of re-purposed datasets and a benchmark for researchers to use. We also propose a contrastive visual representation learning based pre-training workflow which is specialized to masked vs unmasked face matching. We ensure that our method learns robust features to differentiate people across varying data collection scenarios. We achieve this by training over many different datasets and validating our result by testing on various holdout datasets. The specialized weights trained by our method outperform standard face recognition features for masked to unmasked face matching. We believe the provided synthetic mask generating code, our novel training approach and the trained weights from the masked face models will help in adopting existing face recognition systems to operate in the current global environment. We open-source all contributions for broader use by the research community.Repo: https://github.com/sachith500/ContrastiveFaceRepresentation Preprint. Under review.

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“…Countries enforced strict laws and regulations to reduce the transmission of the virus and prevent its spread [2], especially following the lift of the nationwide lockdown. For instance, some counties have made wearing face masks in public mandatory [3,4], including the United Arab Emir-ates (UAE), as it is an efficient way to limit the spread of the virus [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Their data augmentation methodology involves filtering images through greyscale and Gaussian blurring. In [5], the authors utilize YOLOv4 to detect whether pedestrians adhere to the rules of face mask wearing or not, especially at night time. Alok et al [16] proposed CNN and VGG16 model to detect people not wearing a mask.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robots for Deep Mask-Wearing Detection in Educational Settings during Pandemics

Zia

Alhalabi

Yaghi

et al. 2022

Wireless Communications and Mobile Computing

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The COVID-19 pandemic has severely impacted various aspects of life, where countries closed their borders, and workplaces and educational institutions shut down their premises in response to lockdowns. This has adversely affected the lives of everyone, including millions of students worldwide, socially, mentally, and physically. Governments and educational authorities worldwide have taken preventive measures, such as social distancing and mask wearing, to control the spread of the virus. This paper proposes an AI-powered autonomous robot for deep mask-wearing detection to enforce proper mask wearing in educational settings. The system includes (1) Simultaneous Localization and Mapping framework to map and navigate the environment (i.e., laboratories and classrooms), (2) a multiclass face mask detection software, and (3) an auditory system to identify and alert improper or no mask wearing. We train our face mask detector using MobileNetV2 architecture and YOLOv2 object detector classification. The results demonstrate that our robot can navigate an educational environment while avoiding obstacles to detect violations. The proposed face mask detection and classification subsystem achieved a 91.4% average precision when tested on students in an engineering laboratory environment.

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MaskHunter: real‐time object detection of face masks during the COVID‐19 pandemic

Cited by 29 publications

References 46 publications

HRM: An Intelligent Helmet Recognition Model in Complex Scenes

HRM: An Intelligent Helmet Recognition Model in Complex Scenes

Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning

Autonomous Robots for Deep Mask-Wearing Detection in Educational Settings during Pandemics

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