Intelligent Alert System With Yolo V8 Algorithm for Early Detection of Microsleep In Vehicle Drivers

Ria Citra Desiany Putri; Kusumanto Kusumanto; Dewi Permata Sari

doi:10.24014/ijaidm.v8i3.37766

Intelligent Alert System With Yolo V8 Algorithm for Early Detection of Microsleep In Vehicle Drivers

Ria Citra Desiany Putri, Kusumanto Kusumanto, Dewi Permata Sari

Abstract

Microsleep is a brief state of sleep that occurs suddenly without the person being aware of it and poses a serious risk to drivers, especially on long journeys. This study developed an intelligent alert system based on the YOLOv8 algorithm for the early detection of microsleep in drivers in real time by analyzing the state of the eyes and the position of the head. Using 3,458 annotated facial images as training data, the model was implemented on the Raspberry Pi platform for local processing without cloud dependency. The system activates a buzzer and warning light when it detects signs of drowsiness. Test results show the effectiveness of this method in the early detection of microsleep with 90.3% precision, 91.3% accuracy, 96.8% recall, and an F1 score of 93.9%. It has been shown to function optimally in a variety of lighting conditions to improve road safety.

Keywords

Computer Vision; Microsleep; Object Detection; Raspberry Pi; YOLO

Full Text:

PDF

References

World Health Organization, “Road Traffic Injuries,” WHO. 2020 [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries.

A. Bochkovskiy, C-Y. et al., “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv:2004.10934v1 [cs.CV], pp. 1–17, 2020. Available: https://arxiv.org/abs/2004.10934

I. Virgiawan, “Deteksi dan tracking objek secara real-time berbasis computer vision menggunakan metode YOLO V3,” Humantech Jurnal Ilmu Multidisiplin Indonesia., vol. 3 No. 3, 2024, doi: 10.32670/ht.v3i3.4348.

D. A. A. Indra, “Implementasi Algoritma YOLO V5 Detektor pada Pengemudi Kendaraan Bermotor Secara Real-Time,” J. Ilm. KOMPUTASI, vol. 22, pp. 575–582, 2023, doi: 10.32409/jikstik.22.4.3488.

Mihuandayani et al., “Implementasi YOLOv8 untuk Deteksi dan Hitung Objek Manusia dengan Convolutional Neural Network,” Jurnal Buffer Informatika, vol. 11 no. 1, pp 27-35, 2025, doi: 10.25134/buffer.v11i1.356

C. Chen, X. Liu, M. Zhou, Z. Li, Z. Du, and Y. Lin, “Lightweight and Real-Time Driver Fatigue Detection Based on MG-YOLOv8 with Facial Multi-Feature Fusion,” J. Imaging, no. 1, pp. 1–25, 2025.

M. F. Golfantara, “Penggunaan Algoritma YOLOv8 untuk Identifikasi Rempah-Rempah”, Jurnal JITET, vol. 12 no. 3, pp. 3867-3873, 2024, doi: 10.23960/jitet.v12i3S1.5221.

J. Jonathan and D. Hermanto, “Penentuan Epochs Hasil Model Terbaik : Studi Kasus Algoritma YOLOv8,” Digital Transformation Technology, vol. 4, no. 2, pp. 792–798, 2024, doi: 10.47709/digitech.v4i2.4640

E. U. Armin, A. P. Edra, F. I. Alifin, I. Sadidan, I. P. Sary, and U. Latifa, “Performa Model YOLOv8 untuk Deteksi Kondisi Mengantuk pada pengendara mobil,” BRAHMANA, vol. 5, no. 1, pp. 67–76, 2023. DOI:10.30645/brahmana.v5i1.279

R. S. Saputri, et al., “Deteksi Dini Microsleep pada Pengemudi berkendara Roda Empat Melalui Citra Mata Menggunakan Algoritma YOLOv8”, Jurnal Processor, vol. 20 no. 2, pp. 165-179, 2025, doi: 10.33998/processor.2025.20.2.2529

Saepudin, et al., “Analisis Kinerja YOLOv8 Optimalisasi, Roboflow untuk Deteksi Ekspresi Wajah Emosional dengan Machine Learning”, Naratif, Vol. 6 no. 2, 2024, doi: 10.53580/naratif.v6i2.292

P. V. B. Reddy and S. S. Rachana, “DRIVER DROWSINESS MONITORING,” IJCRT, vol. 11, no. 6, pp. 930–934, 2023.

A. I. Moorjani, “Hubungan antara rawan bosan dan kemudahan tertidur dengan microsleep saat mengemudi,” Jurnal Mitra Teknik Sipil, vol. 4, no. 3, pp. 729–736, 2021. Available: https://journal.untar.ac.id/index.php/jmts/article/view/12642

A. Samuel, “Two machine-learning procedures have been investigated in some detail using the game of checkers. Enough work has been done to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by ,” IBM J. Res. Dev., vol. 3, no. 3, pp. 210–229, 1959, doi: 10.1147/rd.33.0210.

R. Stuard, Artificial Intelligence: A Modern Approach (4th ed.). Pearson, 2020. [Online]. Available: https://aima.cs.berkeley.edu/

I. Goodfellow, Deep Learning. MIT Press, 2016. [Online]. Available: https://www.deeplearningbook.org/

N. J. Hayati, “Object Tracking Menggunakan Algoritma You Only Look Once (YOLO)v8 untuk Menghitung Kendaraan”, Jurnal Komputa, vol. 12 no. 2, pp 91-99, 2023, doi: 10.34010/komputa.v12i2.10654

J. Zophie and H. H. Triharminto, “Implemetasi Algoritma You Only Look Once ( YOLO ) menggunakan Web Camera untuk Mendeteksi Objek Statis dan Dinamis Implementation of You Only Look Once ( YOLO ) Algorithm using Web Camera for Static dan Dinamic Object Detection,” Jurnal TNI Angkatan Udara, vol. 1, no. 1, pp. 98–109, 2022, [Online]. Available: https://e-jurnal.tni-au.mil.id/index.php/jpb/article/view/50/44

O. Khare, “YOLOv8-Based Visual Detection of Road Hazards : Potholes , Sewer Covers , and Manholes,” arXiv:2311.00073v1, 2022. [Online]. Available: https://arxiv.org/abs/2311.00073

S. N. Mohamad et al., “Development of Real Time Cat Auto Feeder Dispenser Using Arduino,” International Journal of Synergy Engineering Technologyl., vol. 3, no. 1, pp. 52–57, 2022. [Online]. Available: https://tatiuc.edu.my/ijset/index.php/ijset/article/download/117/99

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once : Unified , Real-Time Object Detection,” CVPR, 2016, doi: 10.1109/CVPR.2016.91.

M. Telçeken and D. Akgun, “An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images,” MDPI, vol. 14, no. 23, pp. 1–16, 2024, doi: https://doi.org/10.3390/app142311293.

Y. Akbar et al., “Deteksi microsleep menggunakan convolutional neural network dengan sensor esp32 pada pengemudi microsleep detection using convolutional neural network with esp32 sensor on the driver,” Intecoms, vol. 7, no. 5, pp. 1668–1674, 2024, doi: 10.31539/intecoms.v7i5.12142.

Ultralytics, “Dataset Preparation Documentation,” Ultralytics.com. 2023. [Online]. Available: https://docs.ultralytics.com/datasets/

E. Syahrudin, E. Utami, and A. D. Hartanto, “Augmentation for Accuracy Improvement of YOLOv8 in Blind Navigation,” Jurnal RESTI, vol. 5, no. 158, pp. 579–588, 2026. doi:10.29207/resti.v8i4.5931

DOI: http://dx.doi.org/10.24014/ijaidm.v8i3.37766

Refbacks

There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Journal Indexing:

IJAIDM Stats