Examining the Performance of a Deep Learning Model Utilizing Yolov8 for Vehicle Make and Model Classification

Year 2024, , 131 - 143, 30.08.2024

Yavuz Ünal , Muzaffer Bolat , Muhammet Nuri Dudak

https://doi.org/10.30931/jetas.1432261

Abstract

Vehicles are important inventions that greatly improve various aspects of human life and find use in almost every field. Once tools are introduced to human existence, they enable time-saving and tasks that are complex or cannot be accomplished by human power. It can be used in situations such as classification of vehicles and tracking of escaped drivers. Tracking the vehicles with the help of brand and model will provide distinctive information to traffic officers. In addition, vehicles of different sizes and functions in traffic can be directed to different lanes. This study examines the use of a YOLOv8 (You Only Look Once version 8) based deep learning model and evaluates its performance for vehicle brand and model classification. YOLOv8 is known as an effective method in the field of object detection and is used in this study to classify the make and model of vehicles. In the classification, 94.3% classification accuracy was achieved.

Keywords

Vehicle make and model recognition, deep learning, YOLOv8, classification

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