Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV
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 throug...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Informatics Department, Faculty of Computer Science Bina Darma University
2024-12-01
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| Series: | Journal of Information Systems and Informatics |
| Subjects: | |
| Online Access: | https://journal-isi.org/index.php/isi/article/view/912 |
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| author | Rizki Juliansyah Muhammad Aqil Musthafa Ar Rachman Muhammad Al Amin Aisya Tyanafisya Nurrizkyta Aulia Hanifah Endang Purnama Giri Gema Parasti Mindara |
| author_facet | Rizki Juliansyah Muhammad Aqil Musthafa Ar Rachman Muhammad Al Amin Aisya Tyanafisya Nurrizkyta Aulia Hanifah Endang Purnama Giri Gema Parasti Mindara |
| author_sort | Rizki Juliansyah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-73a361f3e4ee461fbe04fd92bfefe01d |
| institution | DOAJ |
| issn | 2656-5935 2656-4882 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Informatics Department, Faculty of Computer Science Bina Darma University |
| record_format | Article |
| series | Journal of Information Systems and Informatics |
| spelling | doaj-art-73a361f3e4ee461fbe04fd92bfefe01d2025-08-20T03:01:18ZengInformatics Department, Faculty of Computer Science Bina Darma UniversityJournal of Information Systems and Informatics2656-59352656-48822024-12-01642649267810.51519/journalisi.v6i4.912912Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCVRizki Juliansyah0Muhammad Aqil Musthafa Ar Rachman1Muhammad Al Amin2Aisya Tyanafisya3Nurrizkyta Aulia Hanifah4Endang Purnama Giri5Gema Parasti Mindara6IPB UniversityIPB UniversityIPB UniversityIPB UniversityIPB UniversityIPB UniversityIPB UniversityThis 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.https://journal-isi.org/index.php/isi/article/view/912traffic density monitoringyolov8digital image processingreal-time processingobject detection |
| spellingShingle | Rizki Juliansyah Muhammad Aqil Musthafa Ar Rachman Muhammad Al Amin Aisya Tyanafisya Nurrizkyta Aulia Hanifah Endang Purnama Giri Gema Parasti Mindara Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV Journal of Information Systems and Informatics traffic density monitoring yolov8 digital image processing real-time processing object detection |
| title | Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV |
| title_full | Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV |
| title_fullStr | Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV |
| title_full_unstemmed | Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV |
| title_short | Development of a Real-Time Traffic Density Detection Website Using YOLOv8-Based Digital Image Processing with OpenCV |
| title_sort | development of a real time traffic density detection website using yolov8 based digital image processing with opencv |
| topic | traffic density monitoring yolov8 digital image processing real-time processing object detection |
| url | https://journal-isi.org/index.php/isi/article/view/912 |
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