Automatic recognition of car models - based on Matlab
Zhejiang Jinhua Technology & Trade Polytechnic
Abstract
Based on vehicle classification standards, the construction of autonomous driving modes requires collecting relevant information, including parameters related to vehicle types. Subsequently, this mode can automatically classify vehicles (i.e., vehicle type identification), serving as the foundation for autonomous driving while providing data support for subsequent manual inspections and statistical analysis. The system achieves self-identification of vehicle categories through applications of image processing, image interpretation, and pattern recognition. The main research focuses are on three aspects: image preprocessing, feature extraction of vehicle characteristics, and vehicle type recognition based on BP neural networks. To enhance system accuracy and stability, we propose the following strategies: image enhancement, filtering, and edge detection. Features such as roof length ratio, roof height ratio, and front-rear ratio are extracted. A BP network model is constructed and BP algorithm is implemented. Through training and testing of eight samples from two categories, we have essentially completed preliminary classification of common vehicle types. Systematic testing confirms identification accuracy of 90% for both buses and sedans, demonstrating that our technical capabilities meet practical application requirements across all aspects.
Keywords:
Vehicle type recognition; feature extraction; BP neural networkCopyright Notice & License:
All articles published in the Journal of Engineering Systems and Applications (JESA) are licensed under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0).
Under this license:
-
Attribution — Users must give appropriate credit to the author(s), provide a link to the license, and indicate if any changes were made.
-
Non-Commercial Use — The work may not be used for commercial purposes without explicit written permission from the copyright holder.
-
Adaptations Allowed — Users may remix, transform, or build upon the material for non-commercial purposes, as long as proper attribution is maintained.
-
No Additional Restrictions — Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
-
Third-Party Material — Any third-party material included in the article is subject to the copyright terms of its respective owner, unless otherwise indicated.
Full license text: https://creativecommons.org/licenses/by-nc/4.0/
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PDF Downloads
Journal Info
How to Cite
References
Yuan, Z. (2019). Artificial neural networks and applications. Tsinghua University Press.
Rong, G. (2022). Computer image processing. Tsinghua University Press.
Wang, N., Ren, B., & others. (2021). Automatic recognition of car model images based on neural networks. Chinese Journal of Image and Graphics, (8), 668–672.
Lou, S., & Shi, Y. (2023). System analysis and design based on Matlab – Neural networks. Xi’an University of Electronic Science and Technology Press.
Wang, S. (2023). Matlab 6.5 assists in image processing. Electronic Industry Press.
Yin, G. (2022). Research on the application of fuzzy neural networks in automatic vehicle recognition. China Mechanical Engineering, 13(2), January.
Liu, H., & Bai, Z. (2020). Research on Matlab implementation and application of BP network. Modern Electronic Technology, 2(217).
Zhou, X. (2023). A car model recognition method based on BP neural network. Microelectronics and Computers, (4).
Zhang, T. (2023). Automobile model recognition based on neural networks (Master’s thesis). Beijing Institute of Technology.
Cui, J. (2023). Research on image detection technology for driving vehicles (Master’s thesis). Nanjing University of Aeronautics and Astronautics.
Yao, L., Qin, C., Chen, Q., et al. (2020). Automatic extraction and recognition of road markings based on vehicle laser point cloud. ISPRS Annals, https://doi.org/10.5194/isprs-annals-V-2-2020-313-2020
Liu, F., Lu, Z., & Lin, X. (2025). Vision-based environmental perception for autonomous driving. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 239(1), 39–69. https://doi.org/10.1177/09544070231203059
Li, S., & Yang, S. (2023). Automatic dining car system based on A* algorithm. In Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (Part 2) (pp. 345–350). Xi’an Jiaotong Liverpool University. https://doi.org/10.26914/c.cnkihy.2023.109779
Hyla, T., & Wawrzyniak, N. (2021). Identification of vessels on inland waters using low-quality video streams. Hawaii International Conference on System Sciences.
