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Review

Review of Recent Automated Pothole-Detection Methods

1
C4ISR Systems Development Quality Team, Defense Agency for Technology and Quality, Daejeon 34327, Korea
2
Graduate School of Industry, Kyungpook National University, Daegu 41566, Korea
3
Department of Fine Arts, Kyungpook National University, Daegu 41566, Korea
4
School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(11), 5320; https://doi.org/10.3390/app12115320
Submission received: 17 April 2022 / Revised: 20 May 2022 / Accepted: 20 May 2022 / Published: 24 May 2022

Abstract

Potholes, a kind of road defect, can damage vehicles and negatively affect drivers’ safe driving, and in severe cases can lead to traffic accidents. Efficient and preventive management of potholes in a complex road environment plays an important role in securing driver safety. It is also expected to contribute to the prevention of traffic accidents and the smooth flow of traffic. In the past, pothole detection was mainly performed via visual inspection by human experts. Recently, automated pothole-detection methods apply various technologies that converge basic technologies such as sensors and signal processing. The automated pothole-detection methods can be classified into three types according to the technology used in the pothole-recognition process: a vision-based method, a vibration-based method, and a 3D reconstruction-based method. In this paper, three methods are compared, and the strengths and weaknesses of each method are summarized. The detection process and technology proposed in the latest research related to automated pothole detection are described for each method. The development plans of future technology that is connected with those studies are also presented in this paper.
Keywords: pothole; automated detection; vision; vibration; 3D reconstruction; image processing; deep learning pothole; automated detection; vision; vibration; 3D reconstruction; image processing; deep learning

Share and Cite

MDPI and ACS Style

Kim, Y.-M.; Kim, Y.-G.; Son, S.-Y.; Lim, S.-Y.; Choi, B.-Y.; Choi, D.-H. Review of Recent Automated Pothole-Detection Methods. Appl. Sci. 2022, 12, 5320. https://doi.org/10.3390/app12115320

AMA Style

Kim Y-M, Kim Y-G, Son S-Y, Lim S-Y, Choi B-Y, Choi D-H. Review of Recent Automated Pothole-Detection Methods. Applied Sciences. 2022; 12(11):5320. https://doi.org/10.3390/app12115320

Chicago/Turabian Style

Kim, Young-Mok, Young-Gil Kim, Seung-Yong Son, Soo-Yeon Lim, Bong-Yeol Choi, and Doo-Hyun Choi. 2022. "Review of Recent Automated Pothole-Detection Methods" Applied Sciences 12, no. 11: 5320. https://doi.org/10.3390/app12115320

APA Style

Kim, Y.-M., Kim, Y.-G., Son, S.-Y., Lim, S.-Y., Choi, B.-Y., & Choi, D.-H. (2022). Review of Recent Automated Pothole-Detection Methods. Applied Sciences, 12(11), 5320. https://doi.org/10.3390/app12115320

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