Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Face-Mask Detection with Micro processor

마이크로프로세서 기반의 얼굴 마스크 감지

  • Lim, Hyunkeun (Department of Computer Engineering, Paichai University) ;
  • Ryoo, Sooyoung (Department of Computer Engineering, Paichai University) ;
  • Jung, Hoekyung (Department of Computer Engineering, Paichai University)
  • Received : 2021.01.31
  • Accepted : 2021.03.05
  • Published : 2021.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an embedded system that detects mask and face recognition based on a microprocessor instead of Nvidia Jetson Board what is popular development kit. We use a class of efficient models called Mobilenets for mobile and embedded vision applications. MobileNets are based on a streamlined architechture that uses depthwise separable convolutions to build light weight deep neural networks. The device used a Maix development board with CNN hardware acceleration function, and the training model used MobileNet_V2 based SSD(Single Shot Multibox Detector) optimized for mobile devices. To make training model, 7553 face data from Kaggle are used. As a result of test dataset, the AUC (Area Under The Curve) value is as high as 0.98.

Keywords

References

  1. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, "Ann-Arbor-SSD: Single Shot MultiBox Detector," University of Michigan, 2018.
  2. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," Google Inc., 2018.
  3. Use a Low-Cost Module and MicroPython to Quickly Build AI-Based Vision and Hearing Devices. [Internet]. Available: https://www.digikey.kr/en/articles/use-a-low-cost-module-micropython-ai-based-vision-hearing-devices.
  4. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," Google Inc., 2017.
  5. Face Mask Detection Dataset, [Internet]. Available: https://www.kaggle.com/omkargurav/face-mask-dataset.
  6. S. H. Lee, "Deep learning based face mask recognition for access control," Journal of the Korea Academia-Industrial cooperation Society, vol. 21, no. 8, pp. 395-400, 2020. https://doi.org/10.5762/KAIS.2020.21.8.395
  7. H. B. Yoo, M. S. Park, and S. H. Kim, "REAL TIME FACE DETECTION METHOD USING TENSORRT AND SSD," KIPS Transactions on Software and Data Engineering, vol. 9, no. 10, pp. 323-328, Oct. 2020. https://doi.org/10.3745/KTSDE.2020.9.10.323
  8. M. S. Pak and S. H. Kim, "Face Detection using Deep Learning in Embedded Platform," Proceedings of the Korea Information Processing Society Conference, pp. 827-829, 2018.
  9. H. K. Kim, J. Y. Kim, and H. K. Jung, "Convolutional Neural Network Based Image Processing System," Journal of Information and Communication Convergence Engineering, vol. 16, no. 3, pp. 160-165, Sep. 2018. https://doi.org/10.6109/JICCE.2018.16.3.160