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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Implementation of Drowsy Prevention System Using Arduino and YOLO

아두이노와 YOLO를 이용한 졸음 방지 시스템 구현

  • Lee, Hyun-Ae (School of Computer Information & Communication Engineering, Kunsan National University) ;
  • Shin, Seong-Yoon (School of Computer Information & Communication Engineering, Kunsan National University)
  • Received : 2021.05.25
  • Accepted : 2021.06.30
  • Published : 2021.07.31
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Abstract

In modern society, deaths and property damage due to drowsiness occur every year enormously. Methods to reduce such damage are being studied a lot in all walks of life, and research on preventing drowsy driving is particularly active in automobiles. In this paper, as an Arduino-based water gun firing system that learns open and closed eyes using YOLO, we propose a drowsy prevention system that fires a water gun when the duration of the closed eye exceeds a certain time. This system can be applied and used in various fields, but especially when applied to a car, it is not necessary to purchase expensive specifications and if you pay a little attention, you can reduce accidents caused by drowsy driving by 100% at a very low cost. In addition, it can be said that it is an independent system that overcomes different specifications for each company.

현대 사회에서는 졸음으로 인한 사망사고와 재산피해 등이 해마다 막대하게 발생하고 있다. 이러한 피해를 줄이는 방법들은 사회 각계각층에서 많이 연구하고 있으며 특히, 자동차에서는 졸음운전 방지에 대한 연구가 활발하다. 본 논문에서는 요로(YOLO : You Only Look Once)를 이용하여 뜬눈과 감은 눈을 학습하는 아두이노 기반의 물총 발사를 수행하는 시스템으로써, 단순히 감은 눈의 지속 시간이 일정 시간을 초과하면 물총을 발사하는 졸음 방지 시스템을 제안한다. 본 시스템은 다양한 분야에 적용하여 사용할 수 있지만, 특히, 자동차에 적용 시 비싼 사양을 구매하지 않아도 되고 조금만 신경을 쓰면 아주 저렴한 비용으로도 졸음운전으로 인한 사고를 100% 줄일 수 있다. 또한, 회사별 각기 다른 사양들을 극복한 독립적 시스템이라고 할 수 있다.

Keywords

References

  1. M. Y. Hossain and F. P. George, "IOT based real-time drowsy driving detection system for the prevention of road accidents," in 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), vol. 3, pp. 190-195, Oct. 2018.
  2. S. W. Jang and B. Ahn, "Implementation of detection system for drowsy driving prevention using image recognition and IoT," Sustainability, pp. 3037, Dec. 2020.
  3. I. Chatterjee and A. Sharma, "Driving Fitness Detection: A Holistic Approach For Prevention of Drowsy and Drunk Driving using Computer Vision Techniques," in 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference(SEEDA_CECNSM), pp. 1-6, Sep. 2018.
  4. M. R. B. Shamsuddin, N. N. B. S. Sahar, and M. H. B. Rahmat, "Eye detection for drowsy driver using artificial neural network," in International Conference on Soft Computing in Data Science, Springer, Singapore, pp. 116-125, Nov. 2017.
  5. J. Wang, S. Sun, S. Fang, T. Fu, and J. Stipancic, "Predicting drowsy driving in real-time situations: Using an advanced driving simulator, accelerated failure time model, and virtual location-based services," Accident Analysis & Prevention, vol. 99, Part A, pp. 321-329, Feb. 2017. https://doi.org/10.1016/j.aap.2016.12.014
  6. A. S. Zandi, A. Quddus, L. Prest, and F. J. Comeau, "Non-intrusive detection of drowsy driving based on eye tracking data," Transportation Research Record, vol. 2673, no. 6, pp. 247-257, May. 2019. https://doi.org/10.1177/0361198119847985
  7. H. B. Park and Y. K. Kim, "The research of implementing safety driving system based on camera vision system," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 9, pp. 1088-1095, Sep. 2019. https://doi.org/10.6109/JKIICE.2019.23.9.1088
  8. B. T. Ahn, "Study for Drowsy Driving Detection & Prevention System," Journal of Convergence for Information Technology, vol. 8, no 3, pp. 193-198, Aug. 2018.
  9. H. H. Song and J. K. Choi, "Implementation of ECO Driving Assistance System based on IoT," Journal of The Institute of Internet, Broadcasting and Communication, vol. 20, no. 2, pp. 157-163, Apr. 2020. https://doi.org/10.7236/JIIBC.2020.20.2.157
  10. H. S. Yu, "Design and Implementation of Anti-collision Algorithm Using Bluetooth 4.0 Technology," Journal of Korean Institute of Information Technology, vol. 15, no. 5, pp. 31-36, May. 2017.
  11. S. G. Lee, Y. S. Kwon, J. Park, S. Yun, and W. T. Kim, "A Sleep-driving Accident Prevention System based on EEG analysis using Deep-learning Algorithm," Journal of The Institute of Electronics and Information Engineers, vol. 55, no. 3, pp. 67-73, Mar. 2018. https://doi.org/10.5573/ieie.2018.55.3.67
  12. H. T. Choi, M. K. Back, J. S. Kang, and K. C. Lee, "Driver Drowsiness Detection Based on Visual-Feature Using Multi-Modal Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 07, pp. 1124-1132, Jul. 2018.