Paper
3 October 2024 Intelligent vehicle classification system based on deep learning and multisensor fusion
Xinjin Li, Yuanzhe Yang, Yixiao Yuan, Yu Ma, Yangchen Huang, Haowei Ni
Author Affiliations +
Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327228 (2024) https://doi.org/10.1117/12.3048375
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xinjin Li, Yuanzhe Yang, Yixiao Yuan, Yu Ma, Yangchen Huang, and Haowei Ni "Intelligent vehicle classification system based on deep learning and multisensor fusion", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327228 (3 October 2024); https://doi.org/10.1117/12.3048375
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KEYWORDS
Data modeling

Deep learning

Classification systems

Performance modeling

Feature extraction

Systems modeling

Education and training

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