Vehicle Classification Using Deep Learning-Assisted Triboelectric Sensor
Author:
Funder
Eskişehir Teknik Üniversitesi
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-023-08394-4.pdf
Reference75 articles.
1. Won, M.: Intelligent traffic monitoring systems for vehicle classification: a survey. IEEE Access 8, 73340–73358 (2020). https://doi.org/10.1109/ACCESS.2020.2987634
2. Pang, Y.; Zhu, X.; Yu, Y.; Liu, S.; Chen, Y.; Feng, Y.: Waterbomb-origami inspired triboelectric nanogenerator for smart pavement-integrated traffic monitoring. Nano Res. 15, 5450–5460 (2022). https://doi.org/10.1007/s12274-022-4152-6
3. Abu-Abed, F.; Ivanov, A.: Application of computer modeling software for mining vehicle fleet telemetry monitoring. Acta Montanistica Slovaca (2021). https://doi.org/10.46544/AMS.v26i4.01
4. Kaur, R.; Roul, R.K.; Batra, S.: An efficient approach for accident severity classification in smart transportation system. Arab. J. Sci. Eng. (2022). https://doi.org/10.1007/s13369-022-07274-7
5. Gholamhosseinian, A.; Seitz, J.: Vehicle classification in intelligent transport systems: an overview, methods and software perspective. IEEE Open J. Intell. Transport. Syst. 2, 173–194 (2021). https://doi.org/10.1109/OJITS.2021.3096756
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