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Volume 10
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Communication

A Vehicle Comparison and Re-Identification System Based on Residual Network

by
Weifeng Yin
,
Yusong Min
and
Junyong Zhai
*
School of Automation, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Machines 2022, 10(9), 799; https://doi.org/10.3390/machines10090799
Submission received: 17 August 2022 / Revised: 7 September 2022 / Accepted: 8 September 2022 / Published: 10 September 2022
(This article belongs to the Section Vehicle Engineering)
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Abstract

In the highway intelligent monitoring system, it is difficult to find the target vehicle through millions of pictures because of the presence of fake-licensed vehicles. In order to solve this problem, a vehicle comparison and re-identification (Re-ID) system is built in this paper. By introducing Circle loss and Generalized-Mean(GeM) pooling, vehicle feature extraction and storage, vehicle comparison and vehicle search can be realized. Experimental results show that the proposed algorithm reaches 95.79% of the mean Average Precision (mAP) on the vehicle search task, which meets the requirements of practical applications.
Keywords: vehicle re-identification; deep learning; convolutional neural network (CNN); residual network vehicle re-identification; deep learning; convolutional neural network (CNN); residual network

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MDPI and ACS Style

Yin, W.; Min, Y.; Zhai, J. A Vehicle Comparison and Re-Identification System Based on Residual Network. Machines 2022, 10, 799. https://doi.org/10.3390/machines10090799

AMA Style

Yin W, Min Y, Zhai J. A Vehicle Comparison and Re-Identification System Based on Residual Network. Machines. 2022; 10(9):799. https://doi.org/10.3390/machines10090799

Chicago/Turabian Style

Yin, Weifeng, Yusong Min, and Junyong Zhai. 2022. "A Vehicle Comparison and Re-Identification System Based on Residual Network" Machines 10, no. 9: 799. https://doi.org/10.3390/machines10090799

APA Style

Yin, W., Min, Y., & Zhai, J. (2022). A Vehicle Comparison and Re-Identification System Based on Residual Network. Machines, 10(9), 799. https://doi.org/10.3390/machines10090799

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