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

  • 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
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.

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Published
2024-12-31
Abstract views: 180 times
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How to Cite
Juliansyah, R., Ar Rachman, M., Amin, M., Tyanafisya, A., Hanifah, N., Giri, E., & Mindara, G. (2024). Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV. Journal of Information Systems and Informatics, 6(4), 2649-2678. https://doi.org/10.51519/journalisi.v6i4.912
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Articles