Analysis of Helmet Detection on Motor Drivers to Detect Traffic Violations Using the You Only Look Once Method (Yolov4)

Nadia Hanifa Febriana

doi:10.55927/mudima.v3i7.4931

Authors

Nadia Hanifa Febriana Universitas Pembangunan Nasional “Veteran” Jawa Timur

DOI:

https://doi.org/10.55927/mudima.v3i7.4931

Keywords:

YOLOv4, Deep Learning, Darknet, Breach, Crash

Abstract

According to statistical data, the number of deaths due to accidents in Indonesia in 2017 was 30,568 people. Efforts are being made to reduce traffic violations, especially helmet violations. Helmets that must be worn by Indonesian motorcyclists must comply with the Indonesian National Standard (SNI), but there are still many non-SNI helmets circulating. A possible solution for monitoring is the identification of motorbikes in traffic based on Deep Learning. In this study, the classification of helmets was carried out using the YO-LO (You Only Look Once) method. The SNI helmet detection system aims to make drivers more disciplined in completing their riding equipment, especially helmets with SNI because this system requires riders to wear helmets that comply with LLAJ or SNI (Indonesian National Standard) helmets before riding. Trending Machine Learning and Deep Learning conduct research to discover new methods and advanced architectures such as YOLO (You Only Look Once). YOLO is an object detection network architecture that is claimed to be the "fastest deep learning object detector" that prioritizes accuracy and speed. With YOLOv4, violations by motorbike riders can be detected in real-time and whether the riders recorded on the camera are directly wearing SNI helmets, non-SNI helmets or not wearing helmets. The best accuracy for real-time motorcyclist violations with YOLOv4 is the best mAP value of 99.69%

References

Bisong, E. (2019). 10. Apress, Berkeley, CA. https://doi.org/https://doi.org/10.1007/978-1-4842-4470-8_7

Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. http://arxiv.org/abs/2004.10934

Cui, Zhe and Sun, Hong-Mei and Yin, Ruo-Nan and Gao, Li and Sun, Hai-Bin and Jia, R.-S. (2021). No Title. In Real-time detection method of driver fatigue state based on deep learning of face video.

Fikriya, Z. A., Irawan, M. I., & Soetrisno., S. (2017). Implementasi Extreme Learning Machine untuk Pengenalan Objek Citra Digital. Jurnal Sains dan Seni ITS, 6(1). https://doi.org/10.12962/j23373520.v6i1.21754

Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193–202. https://doi.org/10.1007/BF00344251

Giancini, D., Puspaningrum, E. Y., & Via, Y. V. (2020). Identifikasi Penggunaan Masker Menggunakan Algoritma CNN YOLOv3-Tiny. Prosiding Seminar Nasional Informatika Bela Negara, 1, 153–159. https://doi.org/10.33005/santika.v1i0.41

Haviluddin. (2011). Memahami Penggunaan UML ( Unified Modelling Language ). Memahami Penggunaan UML (Unified Modelling Language), 6(1), 1–15. https://informatikamulawarman.files.wordpress.com/2011/10/01-jurnal-informatika-mulawarman-feb-2011.pdf

Kadir, A. (2013). Buku pintar programmer pemula php.

Khairunnas, K., Yuniarno, E. M., & Zaini, A. (2021). Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot. Jurnal Teknik ITS, 10(1). https://doi.org/10.12962/j23373539.v10i1.61622

Kusuma, T. A. A. H., Usman, K., & Saidah, S. (2021). People Counting for Public Transportations Using You Only Look Once Method. Jurnal Teknik Informatika (Jutif), 2(1), 57–66. https://doi.org/10.20884/1.jutif.2021.2.2.77

Munantri, N. Z., Sofyan, H., & Florestiyanto, M. Y. (2020). Aplikasi Pengolahan Citra Digital Untuk Identifikasi Umur Pohon. Telematika, 16(2), 97. https://doi.org/10.31315/telematika.v16i2.3183

Nugroho, A. (2010). Rekayasa Perangkat Lunak Menggunakan UML & Jav. Yogyakarta :Andi, 2009. http://laser.umm.ac.id/catalog-detail-copy/130001892/

Perkovic, L. (2012). Introduction to Computing Using Python: An Application Development Focus.

Pramestya, R. H. (2018). Deteksi dan Klasifikasi Kerusakan Jalan Aspal Menggunakan Metode YOLO Berbasis Citra Digital. Institut Teknolgi Sepuluh Nopember, 91. http://repository.its.ac.id/59044/1/06111650010019-Master_Thesis.pdf

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Computer Science > Computer Vision and Pattern Recognition. https://doi.org/https://doi.org/10.48550/arXiv.1506.02640

Sinaga, A. S. R. (2017). Implementasi Teknik Threshoding Pada Segmentasi Citra Digital. Jurnal Mantik Penusa, 1(2), 48–51.

Suartika E. P, I Wayan, Wijaya Arya Yudhi, S. R. (2016). Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101. Jurnal Teknik ITS, 5(1), 76. http://repository.its.ac.id/48842/

Wicaksono, B. A., Yuniar Purbasari, I., & Vita Via, Y. (2021). Deteksi Objek Mobil dan Motor pada Lalu Lintas Berbasis Deep Learning. Jurnal Informatika dan Sistem Informasi, 2(2), 334–342. https://doi.org/10.33005/jifosi.v2i2.284

Zulkhaidi, T. C. A.-S., Maria, E., & Yulianto, Y. (2020). Pengenalan Pola Bentuk Wajah dengan OpenCV. Jurnal Rekayasa Teknologi Informasi (JURTI), 3(2), 181. https://doi.org/10.30872/jurti.v3i2.4033