Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

  • Abdul Karim
  • Muhammad Amir Raza
  • Yahya Z. Alharthi
  • Ghulam Abbas
  • Salwa Othmen
  • Md Shouquat Hossain
  • Afroza Nahar
  • Paolo Mercorelli

Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency.

Original languageEnglish
Article number382
JournalWorld Electric Vehicle Journal
Volume15
Issue number9
Number of pages19
DOIs
Publication statusPublished - 09.2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

    Research areas

  • object detection, object tracking, sustainable transportation, traffic incident
  • Engineering

DOI

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