Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks

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-pr...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2006-11, Vol.55 (6), p.1704-1711
Main Authors: KI, Yong-Kul, BAIK, Doo-Kwon
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Back-propagation neural networks
Backpropagation algorithms
Classification
Coils
Computer science
control theory
systems
Connectionism. Neural networks
Detectors
Exact sciences and technology
Frequency
inductive-loop detectors (ILD)
Information, signal and communications theory
Neural networks
Pattern recognition
Pollution measurement
Recognition
Signal processing
Systems, networks and services of telecommunications
Telecommunication traffic
Telecommunications
Telecommunications and information theory
Teletraffic
vehicle classification
Vehicle detection
Vehicles
Waveforms
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Summary: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
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2006.883726