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YOLO-ARGhost: a lightweight face mask detection model

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Abstract

Industrial development can bring huge economic benefits to the country and society. However, it has also caused serious environmental pollution, leading to serious health problems and medical burdens for people, and is often accompanied by the emission of polluting gases. Many companies specify that masks must be worn at work to prevent the inhalation of harmful gases. Quickly detecting whether workers are wearing masks has emerged as a topic of some importance. However, existing face detection networks have limited detection accuracy and considerably reduced performance for masked faces. Hence, most state-of-the-art face mask detection technologies are based on deep learning. In this study, we developed a new feature extraction module called Attention Residual Ghost Module based on attention mechanisms and residual structure. To improve performance, we construct CSP-ARG and ARG-PANert. We then fused attention between the two to obtain a new type of lightweight face mask detection model. The results of an experimental evaluation of the performance of our proposed approach on the public AIZOO and FMDD datasets showed that it achieved accuracy values of 93.4% and 89.3%, respectively, in terms of mAP.

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Data availability

The AIZOO dataset is accessed on https://github.com/AIZOOTech/FaceMaskDetection, and the FMDD dataset is accessed on https://www.kaggle.com/datasets/wobotintelligence/face-mask-detection-dataset, while the code is available for any entity of interest.

References

  1. WH Organization, World Health Statistics (2016) [OP]: Monitoring health for the sustainable development goals (SDGs), World Health Organization, 2016. https://books.google.com.tw/books%3Fhl=zh-CN%26lr=%26id=-A4LDgAAQBAJ%26oi=fnd%26pg=PP1%26dq=Monitoring+health+for+the+sustainable+development+goals%26ots=dcqfZM8nxz%26sig=ioQDFvoetpijoNX3WMnJu8rhLU%26redir_esc=y%23v=onepage%26q=Monitoring%20health%20for%20the%20sustainable%20development%20goals%26f=false

  2. He K, Zhang X, Ren S, Sun J (2016) Identity Mappings in Deep Residual Networks, In: European Conference on Computer Vision, Springer, pp. 630–645

  3. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556

  4. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) YOLOv4: optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934

  5. Bhatti UA, Tang H, Wu G, Marjan S, Hussain A (2023) Deep learning with graph convolutional networks: an overview and latest applications in computational intelligence. Int J Intell Syst 2023:1–28

    Article  Google Scholar 

  6. Bhatti UA, Huang M, Neira-Molina H, Marjan S, Baryalai M, Tang H, Wu G, Bazai SU (2023) MFFCG–multi feature fusion for hyperspectral image classification using graph attention network. Expert Syst Appl 229:120496

    Article  Google Scholar 

  7. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60:84–90

    Article  Google Scholar 

  8. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You Only Look Once: Unified, Real-Time Object Detection, In: 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, June 26-July 1, IEEE Computer Society, Las Vegas, NV, United states, pp. 779–788

  9. Redmon J, Farhadi A (2017) YOLO9000: Better, Faster, STRONGER, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271

  10. J Farhadi (2018) Yolov3: an incremental improvement, arXiv preprint arXiv:1804.02767

  11. Han K, Wang Y, Tian Q, Guo J, Xu C (2020) Ghostnet: More Features from Cheap Operations, In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580–1589

  12. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861

  13. Fan X, Jiang M, (2021) RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic, In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 832–837

  14. Lin TG, Goyal P, Girshick R, He K, Dollár P (2017) Focal Loss for Dense Object Detection, In: IEEE transactions on Pattern Analysis and Machine Intelligence Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988

  15. Singh S, Ahuja U, Kumar M, Kumar K, Sachdeva M (2021) Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimed Tools Appl 80:19753–19768

    Article  Google Scholar 

  16. Li Z, Li H, Hu W, Yang J, Hua Z (2021) Masked face detection model based on multi-scale attention-driven faster R-CNN. J Southwest Jiaotong Univ 56:1002–1010

    Google Scholar 

  17. Jiping Chen Y, Chen J, Darrell T, Malik J (2022) Ghost-YOLO: lightweight masked face detection algorithm. J Signal Process 38:1954–1964

    Google Scholar 

  18. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: Efficient Channel attention for Deep Convolutional Neural Networks, In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, June 1-June 19, IEEE Computer Society, Virtual, Online, United states, pp. 11531–11539

  19. Jaderberg M, Simonyan K, Zisserman A (2015) Spatial transformer networks, Advances in Neural Information Processing Systems, 28, arXiv preprint arXiv:1506.02025

  20. Ioffe S, Szegedy (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, In: International Conference on Machine Learning, PMLR, pp. 448–456

  21. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted Residuals and Linear Bottlenecks, In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520

  22. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path Aggregation Network for Instance segmentation, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–876

  23. Zhang H, Cisse M, Dauphin YN, Lopez-Paz (2017) Mixup: beyond empirical risk minimization, arXiv preprint arXiv:1710.09412

  24. Loshchilov I, Hutter F (2016) Sgdr: stochastic gradient descent with warm restarts, arXiv preprint arXiv:1608.03983

  25. Yang S, Luo P, Loy C-C, Tang X (2016) Wider Face: A Face Detection Benchmark, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533

  26. Ge S, Li J, Ye Q, Luo Z (2017) Detecting Masked Faces in the Wild With lle-cnns, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2682–2690

  27. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: Single Shot Multibox Detector, In: European Conference on Computer Vision, Springer, pp. 21–37

  28. He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778

  29. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely Connected Convolutional networks, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 4700–4708

  30. Jocher G, Chaurasia A, Stoken A, Borovec J, Kwon Y, Michael K, Fang J, Yifu Z, Wong C, Montes D (2022) ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation, Zenodo

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Funding

We appreciate the anonymous reviewers who provided constructive and thoughtful comments that helped improve the manuscript. The authors thank the National Natural Science Foundation of China (Grant Nos.42174164 and 41704132), the key project of Science Technology Department of Sichuan Province (Grant No.2021YJ0358), and the Key Research and Development Support Projects of Chengdu Science and Technology Department (Grant No.2021-YF05-02411-SN) for their financial support.

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Authors and Affiliations

  1. College of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu, 610059, China

    Ruihang Xu, Xuanjing Li & Ruifeng Nie

  2. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China

    Peisen Wang

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  1. Ruihang Xu
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  2. Peisen Wang
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  3. Xuanjing Li
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  4. Ruifeng Nie
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Contributions

All authors conceived the presented idea. All authors developed the theory and performed the computations. All authors verified the analytical methods. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Ruihang Xu.

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Xu, R., Wang, P., Li, X. et al. YOLO-ARGhost: a lightweight face mask detection model. J Supercomput 80, 3162–3182 (2024). https://doi.org/10.1007/s11227-023-05588-3

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