Email this article DETECTION OF LOCAL IRREGULARITIES IN THE ROAD PAVEMENT ON THE BASIS OF WAVELET TRANSFORM OF ULTRASONIC PROFILING DATA
- Authors: Stolbova A.A.1, Prokhorov S.A.1, Golovnin O.K.1
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Affiliations:
- Samara University
- Issue: Vol 7, No 1 (2021): 10.03.2021
- Pages: 34-38
- Section: Articles
- Published: 10.03.2021
- URL: https://dynvibro.ru/dynvibro/article/view/9407
- DOI: https://doi.org/10.18287/2409-4579-2021-7-1-34-38
- ID: 9407
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Abstract
The paper presents an approach to the detection of road pavement defects on the streets and highways based on wavelet analysis of data obtained from an ultrasonic profilometer. The approach makes it possible to determine the location of pavement defects in relation to the road lane. The results of implementing the approach using the complex Morlet wavelet and the first derivative of the Gaussian function are presented. Implementation of the approach reduces the influence of interference arising during ultrasonic diagnosis.
Keywords
About the authors
A. A. Stolbova
Samara University
Author for correspondence.
Email: golovnin@ssau.ru
Russian Federation, Moskovskoe shosse 34,
Samara, 443086
S. A. Prokhorov
Samara University
Email: golovnin@ssau.ru
Russian Federation, Moskovskoe shosse 34,
Samara, 443086
O. K. Golovnin
Samara University
Email: golovnin@ssau.ru
Russian Federation, Moskovskoe shosse 34,
Samara, 443086
References
- Sattar, S., Li, S., Chapman, M. Road surface monitoring using smartphone sensors: A review //Sensors. 2018. V. 18. no. 11. pp. 3845.
- Alqudah, Y. A., Sababha, B. H. On the analysis of road surface conditions using embedded smartphone sensors //2017 8th International Conference on Information and Communication Systems (ICICS). IEEE, 2017. pp. 177-181.
- Koh, J. J., Yap, T. T. V., Ng, H., Goh, V. T., Tong, H. L., Ho, C. C., Kuek, T. Y. Autonomous road potholes detection on video //Computational Science and Technology. Springer, Singapore, 2019. pp. 137-143.
- Anaissi, A., Khoa, N. L. D., Rakotoarivelo, T., Alamdari, M. M., Wang, Y. Smart pothole detection system using vehicle-mounted sensors and machine learning //Journal of Civil Structural Health Monitoring. 2019. V. 9. no. 1. pp. 91-102.
- Kanaeva, I. A., Ivanova, J. A. Road pavement crack detection using deep learning with synthetic data //IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2021. V. 1019. no. 1. pp. 012036.
- Varona, B., Monteserin, A., Teyseyre, A. A deep learning approach to automatic road surface monitoring and pothole detection //Personal and Ubiquitous Computing. 2019. pp. 1-16.
- Nguyen, T. H., Nguyen, T. L., Sidorov, D. N., Dreglea, A. I. Machine learning algorithms application to road defects classification //Intelligent Decision Technologies. 2018. V. 12. no. 1. pp. 59-66.
- Wang, P., Hu, Y., Dai, Y., Tian, M. Asphalt pavement pothole detection and segmentation based on wave-let energy field //Mathematical Problems in Engineering. 2017. V. 2017.
- Golovnin, O.K., Prokhorov, S.A., Stolbova, A.A. Processing time series of measurements based on intel-lectual analysis of wavelet spectra in survey systems for road pavement defects detection / Izvestiya vysshih uchebnyh zavedenij. Povolzhskij region. Tekhnicheskie nauki. 2020. no 4. pp. 47-64.
- Golovnin, O. K., Stolbova, A. A. Wavelet analysis as a tool for studying the road traffic characteristics in the context of intelligent transport systems with incomplete data //Trudy Spiiran. 2019. V. 18. no. 2. pp. 326-353.
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