Computer-Aided Road Inspection: Systems and Algorithms

Rui Fan; Sicen Guo; Li Wang; Mohammud J. Bocus

doi:10.48550/arXiv.2203.02355

Computer-Aided Road Inspection: Systems and Algorithms

Rui Fan
, Sicen Guo
, Li Wang
, Mohammud J. Bocus

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

9 Citations (Scopus)

32 Downloads (Pure)

Abstract

Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.

Original language	English
Title of host publication	Studies in Computational Intelligence
Subtitle of host publication	Recent Advances in Computer Vision Applications Using Parallel Processing
Editors	Khalid M. Hosny, Ahmad Salah
Publisher	Springer, Cham
Chapter	2
Pages	13-39
Number of pages	27
ISBN (Electronic)	978-3-031-18735-3
ISBN (Print)	978-3-031-18734-6
DOIs	https://doi.org/10.48550/arXiv.2203.02355 https://doi.org/10.1007/978-3-031-18735-3_2
Publication status	Published - 24 Jan 2023

Publication series

Name	Studies in Computational Intelligence
Volume	1073
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the National Key R&D Program of China, under grant No. 2020AAA0108100, awarded to Prof. Qijun Chen. This work was also supported by the Fundamental Research Funds for the Central Universities, under projects No. 22120220184, No. 22120220214, and No. 2022-5-YB-08, awarded to Prof. Rui Fan.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

cs.CV

Access to Document

Full-text PDF (accepted manuscript)
This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Springer Nature at https://link.springer.com/chapter/10.1007/978-3-031-18735-3_2. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 1.7 MB

Handle.net

Persistent link

Cite this

Fan, R., Guo, S., Wang, L., & Bocus, M. J. (2023). Computer-Aided Road Inspection: Systems and Algorithms. In K. M. Hosny, & A. Salah (Eds.), Studies in Computational Intelligence: Recent Advances in Computer Vision Applications Using Parallel Processing (pp. 13-39). (Studies in Computational Intelligence; Vol. 1073). Springer, Cham. https://doi.org/10.48550/arXiv.2203.02355, https://doi.org/10.1007/978-3-031-18735-3_2

@inbook{ebcdb46ec1c9442084bd314b74eb286c,

title = "Computer-Aided Road Inspection: Systems and Algorithms",

abstract = "Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.",

keywords = "cs.CV",

author = "Rui Fan and Sicen Guo and Li Wang and Bocus, \{Mohammud J.\}",

note = "Funding Information: Acknowledgements This work was supported by the National Key R\&D Program of China, under grant No. 2020AAA0108100, awarded to Prof. Qijun Chen. This work was also supported by the Fundamental Research Funds for the Central Universities, under projects No. 22120220184, No. 22120220214, and No. 2022-5-YB-08, awarded to Prof. Rui Fan. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

year = "2023",

month = jan,

day = "24",

doi = "10.48550/arXiv.2203.02355",

language = "English",

isbn = "978-3-031-18734-6",

series = "Studies in Computational Intelligence",

publisher = "Springer, Cham",

pages = "13--39",

editor = "\{ Hosny\}, \{Khalid M.\} and Ahmad Salah",

booktitle = "Studies in Computational Intelligence",

address = "Switzerland",

}

Fan, R, Guo, S, Wang, L & Bocus, MJ 2023, Computer-Aided Road Inspection: Systems and Algorithms. in KM Hosny & A Salah (eds), Studies in Computational Intelligence: Recent Advances in Computer Vision Applications Using Parallel Processing. Studies in Computational Intelligence, vol. 1073, Springer, Cham, pp. 13-39. https://doi.org/10.48550/arXiv.2203.02355, https://doi.org/10.1007/978-3-031-18735-3_2

Computer-Aided Road Inspection: Systems and Algorithms. / Fan, Rui; Guo, Sicen; Wang, Li et al.
Studies in Computational Intelligence: Recent Advances in Computer Vision Applications Using Parallel Processing. ed. / Khalid M. Hosny; Ahmad Salah. Springer, Cham, 2023. p. 13-39 (Studies in Computational Intelligence; Vol. 1073).

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

TY - CHAP

T1 - Computer-Aided Road Inspection

T2 - Systems and Algorithms

AU - Fan, Rui

AU - Guo, Sicen

AU - Wang, Li

AU - Bocus, Mohammud J.

N1 - Funding Information: Acknowledgements This work was supported by the National Key R&D Program of China, under grant No. 2020AAA0108100, awarded to Prof. Qijun Chen. This work was also supported by the Fundamental Research Funds for the Central Universities, under projects No. 22120220184, No. 22120220214, and No. 2022-5-YB-08, awarded to Prof. Rui Fan. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023/1/24

Y1 - 2023/1/24

N2 - Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.

AB - Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.

KW - cs.CV

U2 - 10.48550/arXiv.2203.02355

DO - 10.48550/arXiv.2203.02355

M3 - Chapter in a book

SN - 978-3-031-18734-6

T3 - Studies in Computational Intelligence

SP - 13

EP - 39

BT - Studies in Computational Intelligence

A2 - Hosny, Khalid M.

A2 - Salah, Ahmad

PB - Springer, Cham

ER -

Computer-Aided Road Inspection: Systems and Algorithms

Abstract

Publication series

Bibliographical note

Keywords

Access to Document

Handle.net

Fingerprint

Cite this