Development of Object Detection System on Non-Helmed Riders Using YOLOv8

Bima Prihasto; Nisa Rizqiya Fadhliana; Agustina Hariyani; Fauzan M. Alwafi; Tsaqila B. Askarin

doi:10.17509/edsence.v5i2.65910

Development of Object Detection System on Non-Helmed Riders Using YOLOv8

Bima Prihasto, Nisa Rizqiya Fadhliana, Agustina Hariyani, Fauzan M. Alwafi, Tsaqila B. Askarin

Abstract

Motorcycle accidents are a severe problem, with the number of incidents reaching 66,602 in 2023. Helmets as head protection are mandatory, but awareness of their use is still low. This research utilises Deep Learning, specifically YOLOv8, to detect helmet use violations among motorbike riders. The research results show high accuracy with a Precision of 89.5%, Recall at 78.4%, and mAP50 at 85.7%. YOLOv8 effectively detects violations and provides a solid basis for increasing motorist awareness. Through this innovative approach, it is hoped that a safer driving culture and collective awareness of responsibility in traffic safety will be created.

Keywords

YOLOv8; Object Detection; Traffic; Motorcyclists; Helmets

Full Text:

PDF

References

Khoiriyah, K., & Armawan, M. F. A. A. (2023, Juli 2). Deteksi pengendara Motor Tanpa Menggunakan Helm dengan Yolo Algoritma Deep Learning Yolo. Jurnal Elektro & Informatika Swadharma (JEIS), 03(02).

Lee, Y., Im, D., & Shim, J. (2019). Data Labeling Research for Deep Learning Based Fire Detection System. International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), 1-4. 10.1109/SysCoBIoTS48768.2019.9028029.

Mulyana, D. I., & Rowis, M. A. I. (2022). Optimization of Text Mining Detection of Tajweed Reading Laws Using the Yolov8 Method on the Qur'an. QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama, 14(2), 1089-1110.

Prihasto, B., Choirunnisa, S., Nurdiansyah, M. I., Mathulaprangsan, S., Chu, V. C.-M., Chen, S.-H., & Wang, J.-C. (2016). A survey of deep face recognition in the wild. 2016 International Conference on Orange Technologies (ICOT). https://doi.org/10.1109/icot.2016.8278983

Rizqiyah, A. (2023). Angka Kecelakaan Lalu Lintas Terus Meningkat, Usia Pelajar Mendominasi. (2023, September 1). GoodStats. Retrieved November 23, 2023, from https://goodstats.id/article/angka-kecelakaan-lalu-lintas-terus-meningkat-usia-pelajar-mendominasi-zYuep

Setiyadi, A., Utami, E., & Ariatmanto, D. (2023). Analisa Kemampuan Algoritma YOLOv8 Dalam Deteksi Objek Manusia Dengan Metode Modifikasi Arsitektur. Jurnal Sains Komputer & Informatika (J-SAKTI), 7(2), 891-901.

Setyawan, S. B., Pribadi, W., Arrosida, H., & Nugroho, E. P. (2021). Sistem Deteksi Pengendara Sepeda Motor Tanpa Helm dan Kelebihan Penumpang Pada Dengan Menggunakan YOLOv3. Seminar Nasional Terapan Riset Inovatif (SENTRINOV) ke-VII, 7(1).

Wang, G., Chen, Y., An, P., Hong, H., Hu, J., & Huang, T. (2023, Agustus 15). UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios. Sensors. https://doi.org/10.3390/s23167190

YOLO Performance Metrics. (2023, November 12). Ultralytics YOLOv8 Docs. Retrieved November 24, 2023, from https://docs.ultralytics.com/guides/yolo-performance-metrics/#introduction

Zhang, Y., Sun, P., Jang, Y., Yu, D., Weng, F., Yuan, Z., Lio, P., Liu, W., & Xinggang Wang. (2022). ByteTrack: Multi-object Tracking by Associating Every Detection Box. In European Conference on Computer Vision, 1-21.

Zhuang, L., Ding, G., Li, C., & Li, D. (2023, July 29). DCF-Yolov8: An Improved Algorithm for Aggregating Low-Level Features to Detect Agricultural Pests and Diseases. Agronomy. https://doi.org/10.3390/ agronomy13082012

DOI: https://doi.org/10.17509/edsence.v5i2.65910