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Vehicle Classification Using Deep Learning-Assisted Triboelectric Sensor

  • Research Article-Electrical Engineering
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Abstract

Development of a reliable and intelligent traffic monitoring system is highly desired to improve the transportation safety and establish future transportation plans due to the fast growth of vehicle population. Vehicle classification is one of the most critical subsystems where existing ones suffer from privacy concerns, requirements of complicated systems, and high maintenance cost. This paper reports a novel vehicle classification method by utilizing a triboelectric sensor to accurately identify vehicles. Novelty of this method originates from using triboelectric sensor and machine learning method with important advantages over current alternatives by providing an easy installation, simple operation, noninvasive measurement, cost-effective manufacturing, and highly accurate classification. To make a classification, vehicle toys’ signals were acquired from triboelectric sensor and then applied to a deep learning algorithm. The 1932 sensor output data were grouped into a set of seven vehicle toys with different wheelbases, and number of tires passing on are used to train and optimize 1D-CNN model. The utilized 1D-CNN model achieved accuracy, f1-score, precision, and recall as 96.38%, 0.9638, 0.9658, and 0.9637, respectively.

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Acknowledgements

This work was supported by Eskisehir Technical University under grant 20ADP171.

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  1. Electrical and Electronics Engineering Department, Eskisehir Technical University, 26555, Eskisehir, Turkey

    Seval Kinden

  2. Computer Engineering Department, Eskisehir Technical University, 26555, Eskisehir, Turkey

    Zeynep Batmaz

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  1. Seval Kinden
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The manuscript was written through contributions of both authors. S.K. and Z.B. conceived the idea. S.K. fabricated the device, designed the experiment, including the triboelectric sensor output measurements. Z.B. performed experimental analysis for 1D-CNN. Both authors wrote the manuscript and have given approval to the final version of the manuscript.

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Correspondence to Zeynep Batmaz.

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Kinden, S., Batmaz, Z. Vehicle Classification Using Deep Learning-Assisted Triboelectric Sensor. Arab J Sci Eng 49, 6657–6673 (2024). https://doi.org/10.1007/s13369-023-08394-4

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