Vehicle classification plays a pivotal role in traffic management, road safety, and security within intelligent transportation systems. With the increasing deployment of security cameras in smart cities, it becomes essential to develop efficient and reliable methods for vehicle recognition. This research proposes a deep learning-based vehicle classification model using advanced algorithms like YOLOv9 and YOLOv11, which are known for their accuracy and speed in object detection tasks. By leveraging the dataset "Vehicle Detection 3" from Roboflow, collected through security cameras, the model accurately classifies vehicles in images, offering a robust solution for real-time traffic analysis. The proposed method demonstrates superior performance in terms of classification accuracy and processing speed, making it suitable for large-scale deployment in smart city environments. The model's adaptability to various environmental conditions and vehicle types further enhances its application.

Keywords: Vehicle Classification, Deep Learning, YOLOv9, YOLOv11, Object Detection, Intelligent Transportation Systems, Smart Cities, Traffic Management, Security Cameras, Real-time Analysis, Vehicle Recognition.