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Cited by 45Advanced Neural Network ApplicationsFace recognition and analysisVideo Surveillance and Tracking Methods

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SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic

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Han, Zhenggong, Huang, Haisong, Fan, Qingsong, Li, Yiting, Li, Yuqin, Chen, Xingran·2022·Computer Methods and Programs in Biomedicine
DOI 10.1016/j.cmpb.2022.106888PMC PMC9098810
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Abstract

Background and objective At present, the COVID-19 epidemic is still spreading worldwide and wearing a mask in public areas is an effective way to prevent the spread of the respiratory virus. Although there are many deep learning methods used for detecting the face masks, there are few lightweight detectors having a good effect on small or medium-size face masks detection in the complicated environments. Methods In this work we propose an efficient and lightweight detection method based on YOLOv4-tiny, and a face mask detection and monitoring system for mask wearing status. Two feasible improvement strategies, network structure optimization and K-means++ clustering algorithm, are utilized for improving the detection accuracy on the premise of ensuring the real-time face masks recognition. Particularly, the improved residual module and cross fusion module are designed to aim at extracting the features of small or medium-size targets effectively. Moreover, the enhanced dual attention mechanism and the improved spatial pyramid pooling module are employed for merging sufficiently the deep and shallow semantic information and expanding the receptive field. Afterwards, the detection accuracy is compensated through the combination of activation functions. Finally, the depthwise separable convolution module is used to reduce the quantity of parameters and improve the detection efficiency. Our proposed detector is evaluated on a public face mask dataset, and an ablation experiment is also provided to verify the effectiveness of our proposed model, which is compared with the state-of-the-art (SOTA) models as well. Results Our proposed detector increases the AP (average precision) values in each category of the public face mask dataset compared with the original YOLOv4-tiny. The mAP (mean average precision) is improved by 4.56% and the speed reaches 92.81 FPS. Meanwhile, the quantity of parameters and the FLOPs (floating-point operations) are reduced by 1/3, 16.48%, respectively. Conclusions The proposed detector achieves better overall detection performance compared with other SOTA detectors for real-time mask detection, demonstrated the superiority with both theoretical value and practical significance. The developed system also brings greater flexibility to the application of face mask detection in hospitals, campuses, communities, etc.

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Research works printed but not linked: 53

  1. Brooks, 2021, Effectiveness of mask wearing to control community spread of SARS-CoV-2, JAMA- J. Am. Med. Assoc., 325, 998, 10.1001/jama.2021.1505 · DOI
  2. Cao, 2021, Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic, Comput. Methods Programs Biomed., 212, 10.1016/j.cmpb.2021.106468 · DOI
  3. Cheng, 2020, The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2, J. Infect., 81, 107, 10.1016/j.jinf.2020.04.024 · DOI
  4. C. Sun, Z. Zhai, The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission, Sustainable cities and society, 62 (2020) 102390. · DOI
  5. Liu, 2021, Rapid review of social contact patterns during the COVID-19 pandemic, Epidemiology, 32, 781, 10.1097/EDE.0000000000001412 · DOI
  6. Elgendy, 2021, A novel marker detection system for people with visual impairment using the improved tiny-YOLOv3 model, Comput. Methods Programs Biomed., 205
  7. Liu, 2022, All-fiber high-speed image detection enabled by deep learning, Nat. Commun., 13
  8. Yoo, 2021, Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images, Comput. Methods Programs Biomed., 205, 10.1016/j.cmpb.2021.106086 · DOI
  9. Yang, 2021, Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image, IEEE Trans. Cybern.
  10. Yoo, 2020, Toward automated severe pharyngitis detection with smartphone camera using deep learning networks, Comput. Biol. Med., 125
  11. Ge, 2022, Facial expression recognition based on deep learning, Comput. Methods Programs Biomed., 215
  12. Lin, 2020, Face detection and segmentation based on improved mask R-CNN, Discrete Dyn. Nat. Soc., 2020
  13. Ahmed, 2021, IEEE, small scale targeted face detection using deep convolutional neural network, 889
  14. Li, 2021, Enhanced YOLO v3 tiny network for real-time ship detection from visual image, IEEE Access, 9, 16692, 10.1109/ACCESS.2021.3053956 · DOI
  15. Fan, 2021, Beetle antenna strategy based grey wolf optimization, Expert Syst Appl, 165
  16. Fan, 2021, A modified self-adaptive marine predators algorithm: framework and engineering applications, Eng. Comput.
  17. Han, 2020, Small-scale pedestrian detection based on deep neural network, IEEE Trans. Intell. Transp. Syst., 21, 3046, 10.1109/TITS.2019.2923752 · DOI
  18. Besnassi, 2020, Face detection based on evolutionary Haar filter, Pattern Anal. Appl., 23, 309, 10.1007/s10044-019-00784-5 · DOI
  19. Zakaria, 2018, Hierarchical Skin-AdaBoost-Neural Network (H-SKANN) for multi-face detection, Appl. Soft. Comput., 68, 172, 10.1016/j.asoc.2018.03.030 · DOI
  20. J. Redmon, A. Farhadi, Yolov3: an incremental improvement, arXiv preprint, (2018).
  21. A. Bochkovskiy, C.-.Y. Wang, H.-Y.M. Liao, Yolov4: optimal speed and accuracy of object detection, arXiv preprint, (2020).
  22. Z. Ge, S. Liu, F. Wang, Z. Li, J. Sun, Yolox: exceeding yolo series in 2021, arXiv preprint, (2021).
  23. A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: efficient convolutional neural networks for mobile vision applications, arXiv preprint, (2017).
  24. Wang, 2022, TRC-YOLO: a real-time detection method for lightweight targets based on mobile devices, IET Computer Vision, 16, 126, 10.1049/cvi2.12072 · DOI
  25. Lin, 2016, Masked face detection via a modified LeNet, Neurocomputing, 218, 197, 10.1016/j.neucom.2016.08.056 · DOI
  26. Ge, 2017, IEEE, detecting masked faces in the wild with LLE-CNNs, 426
  27. Hussain, 2021, IoT and deep learning based approach for rapid screening and face mask detection for infection spread control of COVID-19, Appl. Sci.-Basel, 11
  28. Loey, 2021, A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic, Measurement, 167, 10.1016/j.measurement.2020.108288 · DOI
  29. Loey, 2021, Fighting against COVID-19: a novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection, Sustain. Cities Soc., 65
  30. Singh, 2021, Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment, Multimed. Tools Appl., 80, 19753, 10.1007/s11042-021-10711-8 · DOI
  31. Wu, 2022, FMD-Yolo: an efficient face mask detection method for COVID-19 prevention and control in public, Image Vis. Comput., 117, 10.1016/j.imavis.2021.104341 · DOI
  32. Su, 2022, Face mask detection and classification via deep transfer learning, Multimed. Tools Appl., 81, 4475, 10.1007/s11042-021-11772-5 · DOI
  33. Cao, 2020, MaskHunter: real-time object detection of face masks during the COVID-19 pandemic, IET Image Process., 14, 4359, 10.1049/iet-ipr.2020.1119 · DOI
  34. Jiang, 2021, Real-time face mask detection method based on YOLOv3, Electronics, 10, 10.3390/electronics10070837 · DOI
  35. Yu, 2021, Face mask wearing detection algorithm based on improved YOLO-v4, Sensors, 21
  36. Nagrath, 2021, SSDMNV2: a real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2, Sustainable Cities and Society, 66
  37. Kumar, 2021, Scaling up face masks detection with YOLO on a novel dataset, Optik, 239, 10.1016/j.ijleo.2021.166744 · DOI
  38. Kumar, 2021, A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system, J. Ambient Intell. Humaniz. Comput.
  39. He, 2015, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell., 37, 1904, 10.1109/TPAMI.2015.2389824 · DOI · Paper page
  40. Roy, 2020, MOXA: A deep learning based unmanned approach for real-time monitoring of people wearing medical masks, Trans. Indian Natl. Acad. Eng., 5, 509, 10.1007/s41403-020-00157-z · DOI
  41. Martinez, 2021, Real-time instance segmentation of traffic videos for embedded devices, Sensors, 21
  42. Chen, 2021, Learning slimming SAR ship object detector through network pruning and knowledge distillation, IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., 14, 1267, 10.1109/JSTARS.2020.3041783 · DOI
  43. Cheng, 2021, Multi-scale safety helmet detection based on SAS-YOLOv3-tiny, Appl. Sci. Basel, 11
  44. Lin, 2022, A detection approach for bundled log ends using K-median clustering and improved YOLOv4-Tiny network, Comp. Electron. Agric., 194
  45. Elfwing, 2018, Sigmoid-weighted linear units for neural network function approximation in reinforcement learning, Neural. Netw., 107, 3, 10.1016/j.neunet.2017.12.012 · DOI · Paper page
  46. Ouyang, 2022, SaccadeFork: A lightweight multi-sensor fusion-based target detector, Inform. Fusion, 77, 172, 10.1016/j.inffus.2021.07.004 · DOI
  47. Kang, 2022, Efficient attention-based deep encoder and decoder for automatic crack segmentation, Struct. Health Monit. Int. J., 10.1177/14759217211053776 · DOI
  48. Roy, 2022, A fast accurate fine-grain object detection model based on YOLOv4 deep neural network, Neural. Comput. Appl., 34, 3895, 10.1007/s00521-021-06651-x · DOI
  49. Vouros, 2021, A semi-supervised sparse K-Means algorithm, Pattern Recognit. Lett., 142, 65, 10.1016/j.patrec.2020.11.015 · DOI
  50. Huang, 2022, Small object detection method with shallow feature fusion network for chip surface defect detection, Sci. Rep., 12, 3914, 10.1038/s41598-022-07654-x · DOI
  51. Su, 2022, Face mask detection and classification via deep transfer learning, Multimed. Tools Appl., 81, 4475, 10.1007/s11042-021-11772-5 · DOI
  52. Hui, 2021, Detail texture detection based on Yolov4-tiny combined with attention mechanism and bicubic interpolation, IET Image Process., 15, 2736, 10.1049/ipr2.12228 · DOI
  53. Liu, 2021, MTI-YOLO: A light-weight and real-time deep neural network for insulator detection in complex aerial images, Energies, 14

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Metadata

Authors
Han, Z., Huang, H., Fan, Q., Li, Y., Li, Y., & Chen, X.
Affiliations
  • Guizhou University (CN)
  • Guiyang Medical University (CN)
Corresponding author
Huang, Haisong
ORCID
Venue
Computer Methods and Programs in Biomedicine
Publisher
Elsevier BV
Published
2022-06
Volume
221
Pages
106888
ISSN
0169-2607 (ISSN-L)
DOI
10.1016/j.cmpb.2022.106888
PMC
PMC9098810
OpenAlex
W4280583405
Cited by
45
References
4
Citations by year
2026: 5 · 2025: 9 · 2024: 16 · 2023: 12 · 2022: 3
FWCI
3.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Computer Methods and Programs in Biomedicine (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (52165063, 51865004)
  • Natural Science Foundation of Guizhou Province (165, 172, 1Y232, 445, 2022, 272, 397)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords
face mask detection, COVID-19 pandemic, YOLOv4-tiny, lightweight object detection, small object detection, K-means clustering, dual attention mechanism, spatial pyramid pooling, depthwise separable convolution, real-time detection
MeSH terms
COVID-19, Algorithms, Humans, Masks, Pandemics

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CoChat. (2026). Citation check: SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic. https://app.cochat.ai/papers/10.1016/j.cmpb.2022.106888
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Han, Z., Huang, H., Fan, Q., Li, Y., Li, Y., & Chen, X. (2022). SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2022.106888