Abstract: Vehicle classification offers intelligent solutions for road traffic monitoring by enabling future prediction planning and decision making. Predictive analytics can be used to predict traffic congestion based on the types of vehicles on the road. In this research, the reliability of deep learning based models for vision-based vehicle classification is investigated. Four models of You Only Look Once (YOLO) are investigated, namely YOLOv5s, YOLOv5x, YOLOv10n, and YOLOv12n. These models were trained and evaluated on a vehicle dataset comprising five vehicle classes, which are Ambulance, Bus, Car, Motorcycle, and Truck, with a total number of 1103 images. From the experiment conducted, YOLOv10n achieved the highest performance measure of mAP@0.5 with 0.859 across all vehicle classes, including per-class evaluation, demonstrating superior detection compared to the other models. Finally, the results indicate that the YOLOv10n model can be used in vision-based vehicle classification.

Ahsiah Ismail, Amelia Ritahani Ismail, Muhammad Afiq Mohd Ara, Asmarani Ahmad Puzi and Suryanti Awang. “Vision-Based Vehicle Classification Using Deep Learning Model”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.6 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160655

@article{Ismail2025,
title = {Vision-Based Vehicle Classification Using Deep Learning Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160655},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160655},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Ahsiah Ismail and Amelia Ritahani Ismail and Muhammad Afiq Mohd Ara and Asmarani Ahmad Puzi and Suryanti Awang}
}