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Vehicle-classification algorithm for single-loop detectors using neural networks
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DOI：10.1109/TVT.2006.883726.png)
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Abstract
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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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