SpringerLink
Log in

Classification of People Both Wearing Medical Mask and Safety Helmet

  • Conference paper
  • First Online:
Smart Applications with Advanced Machine Learning and Human-Centred Problem Design (ICAIAME 2021)

Part of the book series: Engineering Cyber-Physical Systems and Critical Infrastructures ((ECPSCI,volume 1))

  • 515 Accesses

Abstract

The use of medical masks is one of the main measures to combat the Covid-19 epidemic and to prevent and control respiratory diseases caused by the virus. The use of masks is vital to prevent contamination. It is obligatory to wear a helmet to protect the head against possible accidents in work areas where a heavy work is done. While it is compulsory to wear a helmet in such work areas, it has also become mandatory to wear a mask within the scope of combating coronavirus. In this study, the AlexNet-based deep transfer learning method is proposed to determine whether employees comply with the rules of wearing masks and helmets through image analysis. In the proposed study, the images are classified into 4 categories as masked, hard hat, helmet without a mask, and masked helmet. Experiments have been carried out on a single deep net and a double deep neural network and high performance have been achieved. This study can be used effectively and efficiently to determine whether people are wearing helmets and masks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 143.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
GBP 179.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. WHO Coronavirus Disease (COVID-19) Dashboard [Internet]. World Health Organization. [cited 2021 Mar 22]. Available from https://covid19.who.int/

  2. Livingston E, Bucher K (2020) Coronavirus disease 2019 (COVID-19) in Italy. JAMA 323(14):1335

    Article  Google Scholar 

  3. Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep [Internet]. 10(1):1–12. Available from https://doi.org/10.1038/s41598-020-76550-z

  4. Oh Y, Park S, Ye JC (2020) Deep learning COVID-19 features on CXR using limited training data sets. 39(8):2688–700. ar**v

    Google Scholar 

  5. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H et al (2020) Deep learning-based detection for COVID-19 from chest CT using weak label, pp 1–13. medRxiv

    Google Scholar 

  6. Minaee S, Kafieh R, Sonka M, Yazdani S, Jamalipour Soufi G (2020) Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal 65

    Google Scholar 

  7. Eikenberry SE, Mancuso M, Iboi E, Phan T, Eikenberry K, Kuang Y et al (2020) To mask or not to mask: modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect Dis Model [Internet] 5:293–308. Available from https://doi.org/10.1016/j.idm.2020.04.001

  8. Bíl M, Dobiáš M, Andrášik R, Bílová M, Hejna P (2018) Cycling fatalities: when a helmet is useless and when it might save your life. Saf Sci 105:71–76

    Article  Google Scholar 

  9. Fung IWH, Lee YY, Tam VWY, Fung HW (2014) A feasibility study of introducing chin straps of safety helmets as a statutory requirement in Hong Kong construction industry. Saf Sci [Internet] 65:70–8. Available from https://doi.org/10.1016/j.ssci.2013.12.014

  10. Mills NJ, Gilchrist A (1993) Industrial helmet performance in impacts. Saf Sci 16(3–4):221–238

    Article  Google Scholar 

  11. Brolin K, Lanner D, Halldin P (2020) Work-related traumatic brain injury in the construction industry in Sweden and Germany. Saf Sci [Internet] 136:105147. Available from https://doi.org/10.1016/j.ssci.2020.105147

  12. Jamshidi M, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P et al (2019) Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access 2020(8):109581–109595

    Google Scholar 

  13. Arel I, Rose D, Karnowski T (2010) Deep machine learning—a new frontier in artificial intelligence research. IEEE Comput Intell Mag 5(4):13–18

    Article  Google Scholar 

  14. Ivakhnenko AG, Lapa VG (1965) Cybernetic predicting devices. CCM Inf Corp

    Google Scholar 

  15. Fukushima K (1979) Neural network model for a mechanism of pattern recognition unaffected by shift in position—Neocognitron. IEICE Tech Rep A 62(10):658–665

    Google Scholar 

  16. Simard P, LeCun Y, Denker JS (1993) Efficient pattern recognition using a new transformation distance. In: Advances in neural information processing systems, pp 50–8

    Google Scholar 

  17. LeCun Y, Jackel LD, Bottou L, Brunot A, Cortes C, Denker JS et al (1995) Comparison of learning algorithms for handwritten digit recognition. In: International conference on artificial neural networks, pp 53–60

    Google Scholar 

  18. Hinton GE, Dayan P, Frey BJ, Neal RM (1995) The “wake-sleep” algorithm for unsupervised neural networks. Science (80-) 268(5214):1158–61

    Google Scholar 

  19. Aizenberg IN, Aizenberg NN, Vandewalle J (2000) Multiple-valued threshold logic and multi-valued neurons. In: Multi-valued and universal binary neurons. Springer, pp 25–80

    Google Scholar 

  20. Şeker BA, Diri B, Hüseyin H (2017) Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi J Eng Sci 3(3):47–64

    Google Scholar 

  21. Lv Y, Duan Y, Kang W, Li Z, Wang F (2014) Traffic flow prediction with big data : a deep learning approach. IEEE Trans Intell Transp Syst (99):1–9

    Google Scholar 

  22. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  23. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature [Internet] 521:436. Available from https://doi.org/10.1038/nature14539

  24. Bu W, **ao J, Zhou C, Yang M, Peng C (2017) A cascade framework for masked face detection. In: 2017 IEEE international conference on cybernetics and intelligent systems (CIS) and IEEE conference on robotics, automation and mechatronics (RAM), January 2018, pp 458–62

    Google Scholar 

  25. Loey M, Manogaran G, Taha MHN, Khalifa NEM (2020) A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Meas J Int Meas Confed [Internet] 167:108288. Available from https://doi.org/10.1016/j.measurement.2020.108288

  26. Ahmed A, Adeel S, Shahriar H (2020) Face mask detector face mask recognition view project, p 13. Available from https://www.researchgate.net/publication/344173985

  27. Wang Z, Wang G, Huang B, **ong Z, Hong Q, Wu H et al (2020) Masked face recognition dataset and application, pp 1–3. ar**v

    Google Scholar 

  28. Yadav S (2020) Deep learning based safe social distancing and face mask detection in public areas for COVID-19 safety guidelines adherence. Int J Res Appl Sci Eng Technol 8(7):1368–1375

    Article  Google Scholar 

  29. Rohith CA, Nair SA, Nair PS, Alphonsa S, John NP (2019) An efficient helmet detection for MVD using deep learning. In: Proceedings of international conference on trends in electronics and informatics (ICOEI), April 2019, pp 282–6

    Google Scholar 

  30. Li K, Zhao X, Bian J, Tan M (2017) Automatic safety helmet wearing detection. In: 2017 IEEE 7th annual international conference on CYBER technology in automation, control, and intelligent systems (CYBER), pp 617–22

    Google Scholar 

  31. Zhang W, Yang CF, Jiang F, Gao XZ, Zhang X (2020) Safety helmet wearing detection based on image processing and deep learning. In: Proceedings of 2020 international conference on communications, information system and computer engineering (CISCE) (2), pp 343–7

    Google Scholar 

  32. Hariri W (2020) Efficient Masked Face Recognition Method during the COVID-19 pandemic

    Google Scholar 

  33. Sharma V (2018) Face mask detection using YOLOv5 for COVID-19, pp 10–4. Available from https://scholarworks.calstate.edu/downloads/wp988p69r?locale=en

  34. Sandesara AG, Joshi DD, Joshi SD (2020) Facial mask detection using stacked CNN model. Int J Sci Res Comput Sci Eng Inf Technol 3307:264–270

    Google Scholar 

  35. Loey M, Manogaran G, Taha MHN, Khalifa NEM (2020) Fighting against COVID-19: a novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. Sustain Cities Soc [Internet] 65:102600. Available from https://doi.org/10.1016/j.scs.2020.102600

  36. Wu F, ** G, Gao M, He Z, Yang Y (2019) Helmet detection based on improved YOLO V3 deep model. In: Proceedings of 2019 IEEE 16th international conference on networking, sensing and control (ICNSC), pp 363–8

    Google Scholar 

  37. Cao R, Li H, Yang B, Feng A, Yang J, Mu J (2020) Helmet wear detection based on neural network algorithm. J Phys Conf Ser 1650(3)

    Google Scholar 

  38. Ansor A, Ritzkal, Afrianto Y (2020) Mask detection using framework tensorflow and pre-trained CNN model based on raspberry pi. J Mantik 4(3):1539–45

    Google Scholar 

  39. Golwalkar R, Mehendale N (2020) Masked face recognition using deep metric learning and FaceMaskNet-21. SSRN Electron J

    Google Scholar 

  40. Technology I, Stack F, Development S (2020) Real-time masked face recognition using machine learning

    Google Scholar 

  41. Said Y (2020) Pynq-YOLO-Net: an embedded quantized convolutional neural network for face mask detection in COVID-19 pandemic era. Int J Adv Comput Sci Appl 11(9):100–106

    Google Scholar 

  42. Kamboj A, Powar N (2020) Safety helmet detection in industrial environment using deep learning, pp 197–208

    Google Scholar 

  43. Long X, Cui W, Zheng Z (2019) Safety helmet wearing detection based on deep learning. In: Proceedings of 2019 IEEE 3rd information technology, networking, electronic and automation control conference (ITNEC), pp 2495–9

    Google Scholar 

  44. Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2d human pose estimation: new benchmark and state of the art analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3686–93

    Google Scholar 

  45. Real world masked face dataset [Internet]. [cited 2021 Jan 20]. Available from https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset

  46. Safety helmet detection [Internet]. [cited 2021 Jan 20]. Available from https://www.kaggle.com/andrewmvd/hard-hat-detection

  47. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  48. AlexNet [Internet]. [cited 2021 Jan 20]. Available from http://datahacker.rs/deep-learning-alexnet-architecture/

  49. CNN and Softmax [Internet]. [cited 2021 Jan 20]. Available from https://www.andreaperlato.com/aipost/cnn-and-softmax/

  50. Teow MYW (2017) Understanding convolutional neural networks using a minimal model for handwritten digit recognition. In: Proceedings of 2017 IEEE 2nd international conference on automatic control and intelligent systems (I2CACIS), 2017-Decem(October), pp. 167–72

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Department of Software Engineering , Samsun University, 55420, Samsun, Turkey

    Emel Soylu

  2. Faculty of Engineering, Department of Electric-Electronic Engineering, Samsun University, 55420, Samsun, Turkey

    Tuncay Soylu

Authors
  1. Emel Soylu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tuncay Soylu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emel Soylu .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Soylu, E., Soylu, T. (2023). Classification of People Both Wearing Medical Mask and Safety Helmet. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_11

Download citation

Publish with us

Policies and ethics

Search

Navigation