Optimizing Vehicle Detection and Tracking Efficiency Through YOLO-Based Multi-Objective Approach


  • Rabab Farhan Abbas Department of Computer Science, University of Technology – IRAQ, Baghdad , IRAQ
  • Matheel Emaduldeen Abdulmunim Department of Computer Science, University of Technology – IRAQ, Baghdad , IRAQ




Automotive dataset, Image analysis, Vehicle recognition, Vehicle quantification, Images.


In traffic management, a careful detection of cars plays and monitors a vital role in ensuring safety and efficiency. This paper proposes a way to enhance the discovery of vehicles by dividing roads into remote areas and closing. The newly developed approach aims to define a vehicle and calculate vehicles in both fields. Yolov5M network is easy to discover and localize vehicles, exceed the traditional speed tracking methods and enable microorganisms. The proposed form achieves a higher accuracy of 0.833, outperform the Yolov5 Standard Form of 0.67. It also demonstrates the performance enhancement in the results of summons and mean average precision by keeping the training parameters reduction trend the same. Technically speaking, the split of the roads surface is divided into sections using the latest retail techniques and the fine tuning of the Yolov5M network to positively affect the detection and classification of vehicles.


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How to Cite

Farhan Abbas, R., & Emaduldeen Abdulmunim, M. (2024). Optimizing Vehicle Detection and Tracking Efficiency Through YOLO-Based Multi-Objective Approach. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(2), Comp. 62–69. https://doi.org/10.29304/jqcsm.2024.16.21543



Computer Articles