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Sensors 2018, 18(6), 1690; https://doi.org/10.3390/s18061690

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor

1
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 13 April 2018 / Revised: 18 May 2018 / Accepted: 23 May 2018 / Published: 24 May 2018
(This article belongs to the Special Issue Magnetic Sensors)
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Abstract

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance. View Full-Text
Keywords: intelligent transport system; vehicle classification; imbalanced dataset; anisotropic magnetoresistive sensor intelligent transport system; vehicle classification; imbalanced dataset; anisotropic magnetoresistive sensor
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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. (CC BY 4.0).
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Xu, C.; Wang, Y.; Bao, X.; Li, F. Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor. Sensors 2018, 18, 1690.

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