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2022 | OriginalPaper | Chapter

Pavement Distress Detection Using Deep Learning Based Methods: A Survey on Role, Challenges and Opportunities

Authors : Ankit Khatri, Ravi Khatri, Abhishek Kumar, Kuldeep Kumar

Published in: Computing, Communication and Learning

Publisher: Springer Nature Switzerland

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Abstract

Roadways have always been one of the most used modes of transportation, and their contribution to the nation’s economy is also huge. To meet the demands of the growing global population and an increase in urbanization, there has been an exponential rise in the number of vehicles plying on the roads as well as the length of the roads. With this increase in traffic, coupled with other issues like heavy rainfall, the material used for the construction of the road, etc., the condition of the roads deteriorates with cracks and potholes developing on them, which may lead to serious accidents. For effective maintenance of roads and to reduce the associated risks, these defects must be detected. With the advent of Deep Learning (DL) in the recent past and its applications in various sectors, we have comprehensively explored various approaches, particularly using DL in this study, along with the associated challenges in adopting such techniques and future opportunities in this domain. Based on our analysis, using object detection-based models turned out to outperform other approaches.

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Title: Pavement Distress Detection Using Deep Learning Based Methods: A Survey on Role, Challenges and Opportunities
Authors: Ankit Khatri
Ravi Khatri
Abhishek Kumar
Kuldeep Kumar
Publisher: Springer Nature Switzerland
Book: Computing, Communication and Learning
Print ISBN: 978-3-031-21749-4

Electronic ISBN: 978-3-031-21750-0

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-031-21750-0_17

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