Vehicle-classification algorithm for single-loop detectors using neural networks

Yong Kul Ki; Doo Kwon Baik

doi:10.1109/TVT.2006.883726

Vehicle-classification algorithm for single-loop detectors using neural networks

Yong Kul Ki, Doo Kwon Baik

Research output: Contribution to journal › Article › peer-review

92 Citations (Scopus)

Abstract

Vehicle class is an important parameter in the process of road-traffic measurement. Currently, inductive-loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve the accuracy, the authors suggest a new algorithm for ILD using back-propagation neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and frequency waveform. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.5%. The results verified that the proposed algorithm improves the vehicle-classification accuracy compared to the conventional method based on ILD.

Original language	English
Pages (from-to)	1704-1711
Number of pages	8
Journal	IEEE Transactions on Vehicular Technology
Volume	55
Issue number	6
DOIs	https://doi.org/10.1109/TVT.2006.883726
Publication status	Published - 2006 Nov

Keywords

Back-propagation neural networks
Inductive-loop detectors (ILD)
Pattern recognition
Vehicle classification

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Computer Networks and Communications
Electrical and Electronic Engineering

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10.1109/TVT.2006.883726

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abstract = "Vehicle class is an important parameter in the process of road-traffic measurement. Currently, inductive-loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve the accuracy, the authors suggest a new algorithm for ILD using back-propagation neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and frequency waveform. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.5%. The results verified that the proposed algorithm improves the vehicle-classification accuracy compared to the conventional method based on ILD.",

keywords = "Back-propagation neural networks, Inductive-loop detectors (ILD), Pattern recognition, Vehicle classification",

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AU - Baik, Doo Kwon

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AB - Vehicle class is an important parameter in the process of road-traffic measurement. Currently, inductive-loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve the accuracy, the authors suggest a new algorithm for ILD using back-propagation neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and frequency waveform. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.5%. The results verified that the proposed algorithm improves the vehicle-classification accuracy compared to the conventional method based on ILD.

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KW - Inductive-loop detectors (ILD)

KW - Pattern recognition

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Vehicle-classification algorithm for single-loop detectors using neural networks

Abstract

Keywords

ASJC Scopus subject areas

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