• Maxim S. Gorskiy, Moscow technical university of communications and informatics (Moscow, Russia)
• Marina S. Moseva, Moscow technical university of communications and informatics (Moscow, Russia)

This paper aims to describe the development and training of a model capable of classifying vehicles by make and model based on images, as well as an interface for convenient interaction with the model. The novelty of the work is demonstrated by the use of modern vehicle identification methods for fine-grained classification. The paper is divided into three sections, each covering key aspects of the research. The first section provides a domain analysis, reviewing existing vehicle identification methods, including lidar-based technologies, as well as methods using images and videos. Particular attention is paid to the analysis of modern approaches to classifying vehicles by make, model, and other attributes. This analysis allowed us to identify the strengths and weaknesses of various approaches and justify the choice of deep learning architectures for further research. The second section describes the collected dataset with detailed labeling by make and model used for the study. Three machine learning models are compared using various metrics, such as precision, recall, and f1-score. As a result of the analysis, the model that demonstrated the best results for vehicle classification tasks was selected. A quantitative evaluation of the detector used subsequently was also conducted, confirming the effectiveness of the selected model. The third section describes the practical part of the study, which involved augmenting and expanding the dataset. After further training the model on the improved data, the classifier was integrated with the YOLOv11 detector. A web interface was implemented that provides convenient interaction with the system, allowing users to upload videos, view detection and classification results in real time, and analyze statistical data. Testing the system on real-world video surveillance data confirmed the effectiveness of the approach, although it revealed the need for further optimization for complex camera angles and lighting conditions.

vehicle identification, machine learning, YOLO, computer vision, data augmentation

2026-06-05