Investigation of Convolutional Neural Network Model for Vehicle Classification in Smart City

Smart city optimize efficiency by integrating advanced digital technologies, real-time data analytics, and intelligent automation. With the evolution of big data, smart cities enhance infrastructure and provide intelligent solutions for transportation with the integration of high-level adaptability of computer technologies including artificial intelligence (AI). The optimization can be achieved through predictive analytics in providing intelligent solutions for transportation. However, this requires reliable and accurate informative data as input for predictive analytics. Therefore, in this paper, five models of Convolutional Neural Network (CNN) deep learning method are investigated to determine the most accurate model for classification; namely Single Shot Detector (SSD) Resnet50, SSD Resnet152, SSD MobileNet, You Only Look Once (YOLO) YOLOv5 and YOLOv8. A total of 1324 vehicle images are collected to test these CNN models. The images consist of five different categories of vehicles, which are ambulance, car, motorcycle, bus and truck. The performances of all the models are compared. From the evaluation, the model YOLOv8 attained 0.956 of precision, 0.968 of recall and 0.968 of F1 score and outperformed the others. In terms of computational time, YOLOv5 is the fastest. However, a minimal computational time difference is observed between the YOLOv5 and YOLOv8, which were separated by only 20 minutes.

@article{Ismail2025,
  title     = {Investigation of Convolutional Neural Network Model for Vehicle Classification in Smart City},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ahsiah Ismail and Amelia Ritahani Ismail and Nur Azri Shaharuddin and Asmarani Ahmad Puzi and Suryanti Awang},
  doi       = {10.14569/IJACSA.2025.0160489},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160489}
}