Version 1
: Received: 11 July 2024 / Approved: 11 July 2024 / Online: 12 July 2024 (05:55:32 CEST)
How to cite:
Li, X.; Chang, J.; Li, T.; Fan, W.; Ma, Y.; Ni, H. A Vehicle Classification Method Based on Machine Learning. Preprints2024, 2024070981. https://doi.org/10.20944/preprints202407.0981.v1
Li, X.; Chang, J.; Li, T.; Fan, W.; Ma, Y.; Ni, H. A Vehicle Classification Method Based on Machine Learning. Preprints 2024, 2024070981. https://doi.org/10.20944/preprints202407.0981.v1
Li, X.; Chang, J.; Li, T.; Fan, W.; Ma, Y.; Ni, H. A Vehicle Classification Method Based on Machine Learning. Preprints2024, 2024070981. https://doi.org/10.20944/preprints202407.0981.v1
APA Style
Li, X., Chang, J., Li, T., Fan, W., Ma, Y., & Ni, H. (2024). A Vehicle Classification Method Based on Machine Learning. Preprints. https://doi.org/10.20944/preprints202407.0981.v1
Chicago/Turabian Style
Li, X., Yu Ma and Haowei Ni. 2024 "A Vehicle Classification Method Based on Machine Learning" Preprints. https://doi.org/10.20944/preprints202407.0981.v1
Abstract
In this article, we propose a machine learning based fine-grained vehicle classification method VehiClassNet, which effectively solves the challenges of precise vehicle recognition and classification in intelligent transportation systems by combining multi-scale feature extraction, cross modal fusion, and attention mechanisms. Our model demonstrated excellent performance on the Stanford Cars-196 dataset. The innovation of this study lies in its advanced multi-scale feature extraction, cross modal fusion, and attention mechanism. It not only improves classification accuracy, but also lays the foundation for further research on intelligent transportation systems and autonomous vehicle technology.
Keywords
Deep reinforcement learning; Speed planning; Reward function; Fine grained classification; Intelligent transportation system
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
