Version 1
: Received: 25 July 2024 / Approved: 25 July 2024 / Online: 26 July 2024 (06:16:17 CEST)
Version 2
: Received: 26 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (12:30:24 CEST)
How to cite:
Li, X.; Yang, Y.; Yuan, Y.; Ma, Y.; Huang, Y.; Ni, H. Intelligent Vehicle Classification System Based on Deep Learning and Multi-Sensor Fusion. Preprints2024, 2024072102. https://doi.org/10.20944/preprints202407.2102.v2
Li, X.; Yang, Y.; Yuan, Y.; Ma, Y.; Huang, Y.; Ni, H. Intelligent Vehicle Classification System Based on Deep Learning and Multi-Sensor Fusion. Preprints 2024, 2024072102. https://doi.org/10.20944/preprints202407.2102.v2
Li, X.; Yang, Y.; Yuan, Y.; Ma, Y.; Huang, Y.; Ni, H. Intelligent Vehicle Classification System Based on Deep Learning and Multi-Sensor Fusion. Preprints2024, 2024072102. https://doi.org/10.20944/preprints202407.2102.v2
APA Style
Li, X., Yang, Y., Yuan, Y., Ma, Y., Huang, Y., & Ni, H. (2024). Intelligent Vehicle Classification System Based on Deep Learning and Multi-Sensor Fusion. Preprints. https://doi.org/10.20944/preprints202407.2102.v2
Chicago/Turabian Style
Li, X., Yangchen Huang and Haowei Ni. 2024 "Intelligent Vehicle Classification System Based on Deep Learning and Multi-Sensor Fusion" Preprints. https://doi.org/10.20944/preprints202407.2102.v2
Abstract
With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. This study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. The system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes.
Keywords
vehicle classification; deep learning; multi-sensor fusion; convolutional neural network; lidar; gradient boosting decision tree
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.
