Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV

Authors

  • Rizki Juliansyah IPB University, Indonesia
  • Muhammad Aqil Musthafa Ar Rachman IPB University, Indonesia
  • Muhammad Al Amin IPB University, Indonesia
  • Aisya Tyanafisya IPB University, Indonesia
  • Nurrizkyta Aulia Hanifah IPB University, Indonesia
  • Endang Purnama Giri IPB University, Indonesia
  • Gema Parasti Mindara IPB University, Indonesia
Pages Icon

DOI:

https://doi.org/10.51519/journalisi.v6i4.912

Keywords:

Traffic Density Monitoring, YOLOv8, Digital Image Processing, Real-time processing, Object Detection

Abstract

This study introduces a real-time traffic density monitoring system utilizing YOLOv8-based digital image processing to improve traffic management efficiency. By leveraging YOLOv8’s enhanced speed and precision, the system detects and classifies five types of vehicles and displays traffic data through a web interface developed with OpenCV and Flask. Key implementation features include real-time video streaming and accurate detection metrics, with the system achieving 96% Precision, 84% Recall, and an F1 Score of 90% during field testing in Bogor. This indicates the system’s potential for minimizing manual traffic monitoring and aiding traffic authorities in making data-driven decisions. The research also discusses the system’s integration into urban traffic management and its scalability for diverse environments.

Downloads

Download data is not yet available.

References

Tirtowahjono, S. C., and Purwanto, D. G., "Penentuan Distribusi Arus Lalu Lintas Pada Persimpangan Berbasis Teknologi Computer Vision & Deep Learning," Jurnal Dimensi Pratama Teknik Sipil, vol. 11, no. 1, 2022.

S. Xie dan H. Sun, “Tea-YOLOv8s: A Tea Bud Detection Model Based on Deep Learning and Computer Vision,” Sensors, vol. 23, no. 14, Jul 2023, doi: 10.3390/s23146576.

C. Reche, A. Tobias, dan M. Viana, “Vehicular Traffic in Urban Areas: Health Burden and Influence of Sustainable Urban Planning and Mobility,” Atmosphere (Basel), vol. 13, no. 4, Apr 2022, doi: 10.3390/atmos13040598.

T. A. Dompeipen dan S. R. U. A. Sompie, “Penerapan Computer Vision Untuk Pendeteksian Dan Penghitung Jumlah Manusia,” Jurnal Teknik Informatika, vol. 15, no. 4, pp. 1–12, 2021.

J. Azimjonov dan A. Özmen, “A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways,” Advanced Engineering Informatics, vol. 50, pp. 101393, Okt 2021, doi: 10.1016/J.AEI.2021.101393.

D. A. Abdurrafi, M. Taqijjuddin Alawiy, dan B. M. Basuki, “Deteksi Klasifikasi Dan Menghitung Kendaraan Berbasis Algoritma You Only Look Once (YOLO) Menggunakan Kamera CCTV,” SCIENCE ELECTRO, vol. nn, no. 9, 2023.

S. W. J. Hutauruk, T. Matulatan, dan N. Hayaty, “Deteksi Kendaraan Secara Real Time Menggunakan Metode YOLO Berbasis Android,” Jurnal Sustainable: Jurnal Hasil Penelitian Industri Terapan, vol. 09, no. 01, pp. 8–14, 2020.

P. Y. Putra at all., “Deteksi Kendaraan Truk Pada Video Menggunakan Metode Tiny-Yolo V4,” JIP (Jurnal Informatika Polinema), vol. 9, no. 2, pp. 215–2023, 2023.

I. Surya Dharma, “Sistem Rekognisi Jenis Kendaraan Dengan Analisis Video Menggunakan Metode Yolov4,” Kumpulan jurnaL Ilmu Komputer (KLIK), vol. 11, no. 1, 2024.

C. M. Badgujar, A. Poulose, dan H. Gan, “Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review,” Comput Electron Agric, vol. 223, pp. 109090, 2024, doi: 10.1016/j.compag.2024.109090.

J. Redmon, S. Divvala, R. Girshick, dan A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” Jun 2015.

J. R. Terven, D. M. C. Esparza, dan J.-A. Romero-González, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Mach. Learn. Knowl. Extr., vol. 5, pp. 1680–1716, 2023.

C. Li at all., “YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications,” Sep 2022, doi: 10.48550/arXiv.2209.02976.

C.-Y. Wang, A. Bochkovskiy, dan H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” Jul 2022.

N. J. Hayati, D. Singasatia, M. R. Muttaqin, T. Informatika, S. Tinggi, dan T. Wastukancana, “Object Tracking Menggunakan Algoritma You Only Look Once (Yolo)V8 Untuk Menghitung Kendaraan,” KOMPUTA : Jurnal Ilmiah Komputer dan Informatika, vol. 12, no. 2, 2023.

F. Azimah dan K. Wardani, “Sistem Pendeteksi Gejala Awal Covid-19 dengan Penggunaan Metode Al Project Cycle,” Journal Locus Penelitian dan Pengabdian, vol. 1, pp. 405–418, Sep 2022, doi: 10.58344/locus.v1i6.135.

M. R. S. Putri, S. Hermuningsih, dan G. Wiyono, “Pengaruh Profitabilitas, Leverage, Ukuran Perusahaan, dan Pertumbuhan Aset Terhadap Nilai Perusahaan,” Owner, 2024.

A. Faqih, K. Mutmainnah, dan M. R. Afifah, “Seperation Deteksi Kendaraan Pada Citra Digital Dengan Menggunakan Algoritma YOLO (You Only Look Once),” Jurnal Teknik Informatika dan Elektro, vol. 3, no. 2, pp. 5–11, 2021.

Mulajati, M., "Implementasi Teknik Web Scraping dan Klasifikasi Sentimen Menggunakan Metode Naïve Bayes Classifier dan Asosiasi Teks (Studi Kasus: Data Ulasan Penumpang Maskapai Penerbangan Garuda Indonesia pada Situs TripAdvisor)," 2017.

M. K. Suryadewiansyah dan T. E. E. Tju, “Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert,” Jurnal Nasional Teknologi dan Sistem Informasi, 2022, [Daring]. Tersedia pada: https://api.semanticscholar.org/CorpusID:252085210

A. H. Pratomo, W. Kaswidjanti, dan S. Mu’arifah, “Implementation of Region Of Interest (ROI) Algorithm To Improve Car Detection And Classification Algorithm,” Jurnal Teknologi Informasi, vol. 7, no. 1, pp. 155–162, 2020, doi: 10.25126/jtiik.202071718.

Downloads

Published

2024-12-31

Issue

Vol. 6 No. 4 (2024): December

Section

Articles

License

Authors Declaration

  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
  2. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
  3. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
  4. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
  6. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
  7. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
  8. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights

How to Cite

[1]
R. Juliansyah, “Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV”, journalisi, vol. 6, no. 4, pp. 2649–2678, Dec. 2024, doi: 10.51519/journalisi.v6i4.912.

Most read articles by the same author(s)