On the applicability of acoustic sensors in the problem of road surface defects

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Abstract

The article is devoted to the development and substantiation of a multimodal methodology for non-destructive monitoring of the condition of the road surface based on acoustic data supplemented by visual observations. At the level of feature analysis, the study demonstrates that the spectrograms of signals recorded when driving over smooth and damaged surfaces contain stable differences in the time-frequency structure suitable for automatic classification and mapping of defects. The requirements for the type of microphone (sensitivity, bandwidth over 8 kHz, directivity) and placement conditions (height, distance to the source, shielding objects) are justified. A two-sensor architecture with time synchronization combining stereo video and acoustics is proposed; it is shown that the correlation of modalities increases the reliability of localization and the quality of defect classification in real traffic conditions. Taken together, the presented approach forms the basis for long-term, fault-tolerant monitoring systems for road infrastructure with early detection of risks, even with increased noise levels and reduced visibility.

About the authors

Mikhail G. Gorodnichev

Moscow Technical University of Communications and Informatics (MTUCI)

Author for correspondence.
Email: m.g.gorodnichev@mtuci.ru

Cand. Sci. (Eng.), Associate Professor, Dean, Faculty of Information Technology

Russian Federation, Moscow

Grach M. Mkrtchian

Moscow Technical University of Communications and Informatics (MTUCI)

Email: g.m.mkrtchyan@mtuci.ru
ORCID iD: 0000-0002-5802-5513
SPIN-code: 3197-7234

senior lecturer, Department of Software Engineering

Russian Federation, Moscow

Ksenia A. Polyantseva

Moscow Technical University of Communications and Informatics (MTUCI)

Email: k.a.poliantseva@mtuci.ru
ORCID iD: 0000-0002-7102-4208
SPIN-code: 8112-8560

Cand. Sci. (Eng.), associate professor, Department of Data Mining

Russian Federation, Moscow

References

  1. Polyantseva K.A., Gorodnichev M.G. Neural network approaches in the problems of detecting and classifying roadway defects. In: Processing of the Wave Electronics and its Application in Information and Telecommunication Systems (WECONF). St. Petersburg, 2022. Pp. 1–7. doi: 10.1109/WECONF55058.2022.9803392.
  2. Moseva M.S., Gorodnichev M.G., Polyantseva K.A. et al. Development of a platform for road infrastructure digital certification. In: Processing of the Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED). Moscow, Russian Federation, 2021. Pp. 1–8. doi: 10.1109/TIRVED53476.2021.9639102.
  3. Polyantseva K.A. Development of data accumulation algorithms using a stereo pair and detection of road surface defects. Modern High Technologies. 2022. No. 5-1. Pp. 107–112. (In Rus.). doi: 10.17513/snt.39156.
  4. Polyantseva K.A. A high-load platform for aggregation and analysis of unstructured road surface condition data. Industrial Automation. 2022. No. 5. Pp. 32–37. (In Rus.). doi: 10.25728/avtprom.2022.05.09.
  5. Syed S.A., Rashid M., Hussain S., Zahid H. Comparative analysis of CNN and RNN for voice pathology detection. Biomed Res Int. 2021. Vol. 2021. P. 6635964. doi: 10.1155/2021/6635964.
  6. Yin W., Kann K., Yu M., Schütze H. Comparative study of CNN and RNN for natural language processing. arXiv:1702.01923. 2017. URL: https://arxiv.org/abs/1702.01923 (data of accesses: 20.09.2025).
  7. Abu Dabous S., Ait Gacem M., Zeiada W. et al. Artificial intelligence applications in pavement infrastructure damage detection with automated three-dimensional imaging: A systematic review. Alexandria Engineering Journal. 2025. Vol. 117. Pp. 510–533. doi: 10.1016/j.aej.2024.11.081.
  8. Ali Z. A comprehensive overview and comparative analysis of CNN, RNN-LSTM and transformer. SSRN Electronic Journal. 2024. doi: 10.2139/ssrn.5175090.
  9. Zhang X., Huang J., Song E. et al. Design of small MEMS microphone array systems for direction finding of outdoors moving vehicles. Sensors. 2014. Vol. 14. No. 3. Pp. 4384–4398. doi: 10.3390/s140304384.
  10. Jagatheesaperumal S.K., Bibri S.E., Ganesan S. et al. Artificial intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis. Comput. Urban Sci. 2023. Vol. 3. No. 1. P. 28. doi: 10.1007/s43762-023-00104-y.
  11. Brungart D.S., Kordik A.J., Eades C.S., Simpson B.D. The effect of microphone placement on localization accuracy with electronic pass-through earplugs. In: Processing of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. New Paltz, NY, USA, 2003. Pp. 149–152. doi: 10.1109/ASPAA.2003.1285853.
  12. Montes González D., Barrigón Morillas J.M., Rey Gozalo G., Godinho L. Evaluation of exposure to road traffic noise: Effects of microphone height and urban configuration. Environmental Research. 2020. Vol. 191. P. 110055. doi: 10.1016/j.envres.2020.110055.
  13. Montes González D., Barrigón Morillas J. M., Rey Gozalo G. The influence of microphone location on the results of urban noise measurements. Applied Acoustics. 2015. Vol. 90. Pp. 64–73. doi: 10.1016/j.apacoust.2014.11.001.
  14. Srivastava S., Sharma G. Omnivec: Learning robust representations with cross modal sharing. arXiv:2311.05709. 2023. URL: https://arxiv.org/abs/2311.05709 (data of accesses: 21.09.2025).
  15. Chen S., Wu Y., Wang C. et al. Beats: Audio pretraining with acoustic tokenizers. arXiv:2212.09058. 2022. URL: https://arxiv.org/abs/2212.09058 (data of accesses: 21.09.2025).
  16. Mkrtchian G., Polyantseva K. On the use of an acoustic sensor in the tasks of determining defects in the roadway. Systems of Signals Generating and Processing in the Field of on Board Communications. 2024. Vol. 7. No. 1. Pp. 276–280. doi: 10.1109/IEEECONF60226.2024.10496721.

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