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Fatigue and Drowsiness Detection System Using Artificial Intelligence Technique for Car Drivers

Part of the Advanced Structured Materials book series (STRUCTMAT,volume 167)


Road traffic accident in Malaysia is a heavy concern in these days. Among the top factors of traffic accidents, the fatigue and drowsiness of drivers often times contributed to the increasing number of cases and fatality rate of accidents. This research aims to develop a computer vision system to detect such fatigue and drowsiness of the drivers and wake them up from the split-second nap. The implementation of this research is to develop a drowsiness detection system implemented in a compact development board to assist drivers to awaken from microsleep during driving on fatigue due to long driving hours and various other reasons. This research used a Raspberry Pi 4 along with the official Raspberry Pi camera module V2 and an active buzzer module as waking mechanism for the system. The development used and experimented on the Haar cascade classifier and Histogram of Oriented Gradient + linear Support Vector Machine in the effort of determining the best suitable model to be used for drowsiness detection in terms of speed and accuracy. Both models were run and tested to work properly. The implementation of the Haar cascade classifier produced the best performance in terms of speed and response time to detect drowsiness. On the other hand, the HOG + SVM had better accuracy when compared to the Haar cascade classifier even in low illumination. Having said that, the response time is significantly slower than Haar model which caused a problem regarding the reaction time of drivers to react on time. To conclude, the Haar cascaded classifier is decided as the most appropriate model to be applied for the development of a drowsiness detection system.


  • Computer vision
  • Applied AI
  • Artificial intelligence
  • Eye aspect ratio
  • Embedded system

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  1. Bernama (2017) “Don’t drive if you are fatigued, sleepy—health DG,” Accessed 14 Jun 2020

  2. Zainal AANS, Mohd FSA, Lamin F, Abdul MAR (2012) MIROS crash investigation and reconstruction annual statistical report 2007–2010

    Google Scholar 

  3. Cech J, Soukupova T (2016) “Real-time eye blink detection using facial landmarks,” Cent Mach Perception, Dep Cybern Fac Electr Eng Czech Tech Univ Prague, pp 1–8.

  4. Farley P (2019) “Face detection and attributes concepts—azure cognitive services | microsoft docs,”. . Accessed 16 Jun 2020

  5. Dwivedi D (2018) “Face detection for beginners—towards data science,” . Accessed 15 Jun 2020

  6. Vanderplas J (2016) “Python data science handbook,” . Accessed 15 Jun 2020

  7. Berger W (2020) “Deep learning haar cascade explained.” Accessed 15 Jun 2020

  8. Dalal N, Triggs B (2018) “Histogram of oriented gradients for human detection,” Proc 2018 5th Int Conf Bus Ind Res Smart Technol Next Gener Information, Eng Bus Soc Sci ICBIR 2018.

  9. Frigerio A, Hadlock TA, Murray EH, Heaton JT (2014) Infrared-based blink-detecting glasses for facial pacing: toward a bionic blink. JAMA Facial Plast Surg 16(3):211–218.

    CrossRef  Google Scholar 

  10. Deshmukh SV et al (2017) Face detection and face recognition using raspberry pi. Ijarcce 6(4):70–73.

    CrossRef  Google Scholar 

  11. Gupta I, Patil V, Kadam C, Dumbre S (2017) “Face detection and recognition using raspberry pi,” WIECON-ECE 2016–2016 IEEE Int WIE Conf Electr Comput Eng, pp 83–86.

  12. Kwolek B (2014) “Face detection using convolutional neural networks and gabor filters,” Lect. Notes Comput Sci 3696.

  13. Kamencay P, Benco M, Mizdos T, Radil R (2017) “A new method for face recognition using convolutional neural network,” Adv Electr Electron Eng 15(4): 663–672.

  14. Reddy B, Kim Y H, Yun S, Seo C, Jang J (2017) “Real-time driver drowsiness detection for embedded system using model compression of deep neural networks.” In IEEE computer society conference on computer vision and pattern recognition workshops.

  15. Zhang K, Zhang Z, Li Z, Qiao Y (2016) “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Process Lett.

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Author would like to acknowledge Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur for the funding provided.

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Correspondence to Mohd Azlan Abu .

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Abu, M.A., Ishak, I.D., Basarudin, H., Ramli, A.F., Shapiai, M.I. (2022). Fatigue and Drowsiness Detection System Using Artificial Intelligence Technique for Car Drivers. In: Ismail, A., Dahalan, W.M., Öchsner, A. (eds) Design in Maritime Engineering. Advanced Structured Materials, vol 167. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89987-5

  • Online ISBN: 978-3-030-89988-2

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