Sistem Deteksi Kantuk Pengemudi Mobil Berdasarkan Analisis Rasio Mata Menggunakan Computer Vision

Authors

  • Andi Asvin Mahersatillah Suradi Universitas Dipa Makassar
  • Samsu Alam Universitas Dipa Makassar
  • Mushaf Universitas Dipa Makassar
  • Muhammad Furqan Rasyid Universitas Dipa Makassar
  • Imran Djafar Universitas Dipa Makassar

DOI:

https://doi.org/10.53842/juki.v5i2.269

Keywords:

Drowsiness Detection, HOG, Linear SVM, Face Detection

Abstract

Driver drowsiness is one of the main causes of motor vehicle accidents. According to National Sleep Foundation records, about 32% of drivers have at least one drowsy driving experience per month. About 25% of traffic accidents are caused by drowsiness while driving each year. The purpose of this study is to design a system that can detect driver sleepiness based on the aspect ratio of the eye with certain parameters using a webcam placed in the car's speedometer area. The methods used are Histogram Oriented Gradients (HOG) and Linear SVM which are in the dlib library which includes machine learning algorithms and uses real time applications. A pre-trained facial landmark detector from the dlib library is used to predict the location of the 68 x-y coordinates that map the facial landmarks to the face zones. The results of this study indicate that the system can be used in real time to detect driver drowsiness with the camera position in the speedometer area at a distance of 50 cm with an average accuracy of 90.4%.

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References

R. Jabbar, K. Al-Khalifa, M. Kharbeche, W. Alhajyaseen, M. Jafari, and S. Jiang, “Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques,” Procedia Comput. Sci., vol. 130, pp. 400–407, 2018, doi: 10.1016/j.procs.2018.04.060.

“Angka Kecelakaan Masih Tinggi, Menhub: Kolaborasi Jadi Kunci Peningkatan Keselamatan Jalan,” BIRO KOMUNIKASI DAN INFORMASI PUBLIK, 2022. [Online]. Available: https://dephub.go.id/post/read/angka-kecelakaan-masih-tinggi,-menhub-kolaborasi-jadi-kunci-peningkatan-keselamatan-jalan. [Accessed: 11-Aug-2022].

F. You, X. Li, Y. Gong, H. Wang, and H. Li, “A Real-time Driving Drowsiness Detection Algorithm with Individual Differences Consideration,” IEEE Access, vol. 7, pp. 179396–179408, 2019, doi: 10.1109/ACCESS.2019.2958667.

C. Jacobé de Naurois, C. Bourdin, A. Stratulat, E. Diaz, and J. L. Vercher, “Detection and prediction of driver drowsiness using artificial neural network models,” Accid. Anal. Prev., vol. 126, no. October 2017, pp. 95–104, 2019, doi: 10.1016/j.aap.2017.11.038.

M. Ahmad Kamran, M. M. N. Mannan, and M. Y. Jeong, “Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study,” IEEE Access, vol. 7, pp. 167172–167186, 2019, doi: 10.1109/ACCESS.2019.2951028.

G. Soares, D. De Lima, and A. Miranda Neto, “A Mobile Application for Driver’s Drowsiness Monitoring based on PERCLOS Estimation,” IEEE Lat. Am. Trans., vol. 17, no. 2, pp. 193–202, 2019, doi: 10.1109/TLA.2019.8863164.

P. D. Purnamasari, P. Yustiana, A. A. Putri Ratna, and D. Sudiana, “Mobile EEG Based Drowsiness Detection using K-Nearest Neighbor,” 2019 IEEE 10th Int. Conf. Aware. Sci. Technol. iCAST 2019 - Proc., no. June, pp. 1–5, 2019, doi: 10.1109/ICAwST.2019.8923161.

A. A. M. Suradi, M. F. Rasyid, M. Mushaf, and M. Rizal, “Deteksi Tingkat Kematangan Buah Apel Menggunakan Segmentasi Ruang Warna HSV,” in Seminar Ilmiah Sistem Informasi dan Teknologi Informasi, 2023, vol. XII, no. 1, pp. 19–26.

S. Mohamad Idris, Romindo, Muhammad Munsarif, G. F. M. Wa Ode Rahma Agus Udaya Manarfa, N. Andi Asvin Mahersatillah Suradi, Lutfi Hakim, M. F. V. R. Arsan Kumala Jaya, and A. A. Andrew Tanny Liem, Pengolahan Citra: Teori dan Implementasi. Medan: Yayasan Kita Menulis, 2023.

A. A. M. Suradi, Z. Zainuddin, and Y. Yusran, “Deteksi Jalan Berdasarkan Segmentasi Warna HSV Dalam Penerapan Mobil Otonom (Autonomous Car),” Universitas Hasanuddin, 2021.

Z. Zainuddin, A. A. M. Suradi, and E. Warni, “Unstructured road detection segmentation for autonomous car,” in AIP Conference Proceedings, 2022, doi: https://doi.org/10.1063/5.0095775.

O. Adeyemi, M. Irhebhude, and A. Kolawole, “Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques,” Adv. Image Video Process., vol. 7, no. 5, pp. 30–42, 2019, doi: 10.14738/aivp.75.7205.

R. G, “A Study to Find Facts Behind Preprocessing on Deep Learning Algorithms,” J. Innov. Image Process., vol. 3, no. 1, pp. 66–74, 2021, doi: 10.36548/jiip.2021.1.006.

A. N. N. Afifah, Indrabayu, A. Suyuti, and Syafaruddin, “Hotspot Detection in Photovoltaic Module using Otsu Thresholding Method,” 2020 IEEE Int. Conf. Commun. Networks Satell. Comnetsat 2020 - Proc., no. March 2021, pp. 408–412, 2020, doi: 10.1109/Comnetsat50391.2020.9328987.

A. A. M. Suradi and A. Syarwani, “Sistem Absensi Menggunakan Teknologi Qr Code Dan Face,” e-Jurnal JUSITI (Jurnal Sist. Inf. dan Teknol. Informasi), vol. 10, no. 1, pp. 62–73, 2021, doi: 10.36774/jusiti.v10i1.821.

B. K. Savas and Y. Becerikli, “Real time driver fatigue detection based on SVM Algorithm,” 2018 6th Int. Conf. Control Eng. Inf. Technol. CEIT 2018, no. October, pp. 25–27, 2018, doi: 10.1109/CEIT.2018.8751886.

T. Soukupová and J. Cech, “Eye Blink Detection Using Facial Landmarks,” Res. Reports C., pp. 1–8, 2016.

Indrabayu, S. K. Mufti, and I. S. Areni, “Car Driver Drowsiness Recognition Android-Based System,” IOP Conf. Ser. Mater. Sci. Eng., vol. 619, no. 1, 2019, doi: 10.1088/1757-899X/619/1/012021.

S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behav. Processes, vol. 148, pp. 56–62, Mar. 2018, doi: 10.1016/j.beproc.2018.01.004.

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Published

2023-11-04

How to Cite

Suradi, A. A. M. ., Alam, S. ., Mushaf, M., Rasyid, M. F. ., & Djafar, I. . (2023). Sistem Deteksi Kantuk Pengemudi Mobil Berdasarkan Analisis Rasio Mata Menggunakan Computer Vision. JUKI : Jurnal Komputer Dan Informatika, 5(2), 222–230. https://doi.org/10.53842/juki.v5i2.269