Mobile Application Utilizing YOLOv8 for Real-Time Urban Traffic Data Collection
This paper presents a pioneering mobile application specifically designed to revolutionize the collection of urban traffic data. The application is engineered on the robust YOLOv8 platform for mobile devices, leveraging smartphone cameras to provide real-time observations of traffic conditions and v...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00077.pdf |
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| Summary: | This paper presents a pioneering mobile application specifically designed to revolutionize the collection of urban traffic data. The application is engineered on the robust YOLOv8 platform for mobile devices, leveraging smartphone cameras to provide real-time observations of traffic conditions and vehicle counts with a notable accuracy of 93%. The development environment includes Java Android, enhancing the app with cutting-edge functionalities such as YOLOv8 for precise vehicle type detection and Deep Sort for effective vehicle counting. Despite achieving high accuracy, the application encounters difficulties in accurately detecting motorcycles, which are often hidden behind larger vehicles. This research not only demonstrates the effectiveness of a sophisticated tool for traffic data analysis but also emphasizes the essential need for continuous improvements to tackle practical challenges encountered in urban settings. The findings signify a major progression in urban mobility research and advocate for advanced traffic management and planning strategies. |
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| ISSN: | 2267-1242 |