Design of Emergency Alarm System for Drowsiness Detection Using YOLO Method Based on Raspberry Pi

Dinda Ayu Permatasari, Gillang Al Azhar, Muhammad Rifqi Zharfan, Anindya Dwi Risdhayanti, Arief Rahman Hidayat, Denda Dewatama

Abstract

Drowsiness is one of the main factors causing traffic accidents that often lead to fatalities, as drowsy drivers lose concentration. Therefore, drowsiness detection in car drivers is very important to prevent accidents. In this research, an emergency alarm system for drowsiness detection using YOLO method based on Mini PC is designed. This drowsiness detection system uses a camera to take pictures of the driver's face and the YOLO algorithm to detect the eyes. If the driver's eyes are detected to be closed, the system will give a warning in the form of a buzzer, display on the LCD, and water spray to wake up the driver. Test results show that the system has an average accuracy rate of 88% under optimal lighting conditions and 66.8% under low lighting conditions. The system also records detection and response times for further analysis, demonstrating the ability to not only detect the driver's condition but also record the time of the event. The data results show that the system is capable of detecting “Awake”, “Drowsy”, and “Asleep” states with an accuracy rate of 80%, as well as providing effective warnings to the driver when it detects signs of drowsiness.

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References

R. T. Puteri and F. Utaminingrum, “Deteksi Kantuk Menggunakan Kombinasi Haar Cascade dan Convolutional Neural Network,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 3, pp. 816–821, 2020.

N. Ramadhani, S. Aulia, E. Suhartono, and S. Hadiyoso, “Deteksi Kantuk pada Pengemudi Berdasarkan Penginderaan Wajah Menggunakan PCA dan SVM,” Jurnal Rekayasa Elektrika, vol. 17, no. 2, pp. 129–136, 2021, doi: 10.17529/jre.v17i2.19884.

C. Aj. Saputra, D. Erwanto, and P. N. Rahayu, “Deteksi Kantuk Pengendara Roda Empat Menggunakan Haar Cascade Classifier Dan Convolutional Neural Network,” JEECOM Journal of Electrical Engineering and Computer, vol. 3, no. 1, pp. 1–7, 2021, doi: 10.33650/jeecom.v3i1.1510.

K. Khairunnas, E. M. Yuniarno, and A. Zaini, “Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot,” Jurnal Teknik ITS, vol. 10, no. 1, 2021, doi: 10.12962/j23373539.v10i1.61622.

M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” Jurnal Algoritme, vol. 1, no. 1, pp. 45–56, 2020, doi: 10.35957/algoritme.v1i1.434.

D. A. Permatasari, H. Fakhrurroja, and C. Machbub, “Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method,” Int J Adv Sci Eng Inf Technol, vol. 10, no. 5, 2020.

J. Zophie and H. H. Triharminton, “Implemetasi Algoritma You Only Look Once (YOLO) menggunakan Web Camera untuk Mendeteksi Objek Statis dan Dinamis,” TNI Angkatan Udara, vol. 1, no. 1, 2023, doi: 10.62828/jpb.v1i1.50.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, doi: 10.1109/CVPR.2016.91.

D. Ayu Permatasari and D. Anggi Maharani, “Backpropagation Neural Network for Tuning PID Pan- Tilt Face Tracking,” Proceedings of the 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), pp. 357–361, Nov. 2018.

A. Kholik, “Klasifikasi Menggunakan Convolutional Neural Network (Cnn) Pada Tangkapan Layar Halaman Instagram,” Jurnal Data Mining dan Sistem Informasi, vol. 2, no. 2, p. 10, 2021, doi: 10.33365/jdmsi.v2i2.1345.

G. Aprilian Anarki, K. Auliasari, and M. Orisa, “Penerapan Metode Haar Cascade Pada Aplikasi Deteksi Masker,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 5, no. 1, pp. 179–186, 2021, doi: 10.36040/jati.v5i1.3214.

S. Jupiyandi, F. R. Saniputra, Y. Pratama, M. R. Dharmawan, and I. Cholissodin, “Pengembangan Deteksi Citra Mobil Untuk Mengetahui Jumlah Tempat Parkir Menggunakan Cuda Dan Modified Yolo Development of Car Image Detection To Find Out the Number of Parking Space Using Cuda and Modified Yolo,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 6, no. 4, pp. 413–419, 2019, doi: 10.25126/jtiik.201961275.

P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (CNN) Pada Ekspresi Manusia,” Algor, vol. 2, no. 1, pp. 12–21, 2020.

M. Sutrisno, Deteksi Kantuk Berbasis Jetson Nano Bagi Pengemudi Roda Empat Menggunakan Yolov5. 2022.

S. Maslikah, R. Alfita, and A. F. Ibadillah, “Sistem Deteksi Kantuk Pada Pengendara Roda Empat Menggunakan Eye Blink Detection,” Jurnal FORTECH, vol. 1, no. 1, pp. 33–38, 2020.

M. R. S. Alfarizi, M. Z. Al-farish, M. Taufiqurrahman, G. Ardiansah, and M. Elgar, “Penggunaan Python Sebagai Bahasa Pemrograman untuk Machine Learning dan Deep Learning,” Karya Ilmiah Mahasiswa Bertauhid (KARIMAH TAUHID), vol. 2, no. 1, pp. 1–6, 2023.

DOI: https://doi.org/10.20961/jeeict.6.2.93201

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