A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images

Ibrahim Hassan Syed; Susan McKeever; Kieran Feighan; David Power; Dympna O’Sullivan

doi:10.1007/978-3-031-44137-0_18

A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images

Ibrahim Hassan Syed, Susan McKeever, Kieran Feighan, David Power, Dympna O’Sullivan

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for significant cost reduction in inspections, improved safety conditions during checks, and acceleration of the current manual inspection processes.

Original language	English
Title of host publication	Computer Vision Systems - 14th International Conference, ICVS 2023, Proceedings
Editors	Henrik I. Christensen, Peter Corke, Renaud Detry, Jean-Baptiste Weibel, Markus Vincze
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	208-219
Number of pages	12
ISBN (Print)	9783031441363
DOIs	https://doi.org/10.1007/978-3-031-44137-0_18
Publication status	Published - 2023
Event	14th International Conference on Computer Vision Systems, ICVS 2023 - VIenna, Austria Duration: 27 Sep 2023 → 29 Sep 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14253 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Computer Vision Systems, ICVS 2023
Country/Territory	Austria
City	VIenna
Period	27/09/23 → 29/09/23

Keywords

deep learning
Object detection
Road surface
visual inspection

Access to Document

10.1007/978-3-031-44137-0_18

https://arrow.tudublin.ie/scschcomcon/419Licence: CC BY-NC-SA

Cite this

Syed, I. H., McKeever, S., Feighan, K., Power, D., & O’Sullivan, D. (2023). A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images. In H. I. Christensen, P. Corke, R. Detry, J.-B. Weibel, & M. Vincze (Eds.), Computer Vision Systems - 14th International Conference, ICVS 2023, Proceedings (pp. 208-219). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14253 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44137-0_18

Syed, Ibrahim Hassan ; McKeever, Susan ; Feighan, Kieran et al. / A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images. Computer Vision Systems - 14th International Conference, ICVS 2023, Proceedings. editor / Henrik I. Christensen ; Peter Corke ; Renaud Detry ; Jean-Baptiste Weibel ; Markus Vincze. Springer Science and Business Media Deutschland GmbH, 2023. pp. 208-219 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b84975409b0c4c52a5ee51f5b843698a,

title = "A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images",

abstract = "Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for significant cost reduction in inspections, improved safety conditions during checks, and acceleration of the current manual inspection processes.",

keywords = "deep learning, Object detection, Road surface, visual inspection",

author = "Syed, {Ibrahim Hassan} and Susan McKeever and Kieran Feighan and David Power and Dympna O{\textquoteright}Sullivan",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 14th International Conference on Computer Vision Systems, ICVS 2023 ; Conference date: 27-09-2023 Through 29-09-2023",

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doi = "10.1007/978-3-031-44137-0_18",

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}

Syed, IH, McKeever, S, Feighan, K, Power, D & O’Sullivan, D 2023, A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images. in HI Christensen, P Corke, R Detry, J-B Weibel & M Vincze (eds), Computer Vision Systems - 14th International Conference, ICVS 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14253 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 208-219, 14th International Conference on Computer Vision Systems, ICVS 2023, VIenna, Austria, 27/09/23. https://doi.org/10.1007/978-3-031-44137-0_18

A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images. / Syed, Ibrahim Hassan; McKeever, Susan; Feighan, Kieran et al.
Computer Vision Systems - 14th International Conference, ICVS 2023, Proceedings. ed. / Henrik I. Christensen; Peter Corke; Renaud Detry; Jean-Baptiste Weibel; Markus Vincze. Springer Science and Business Media Deutschland GmbH, 2023. p. 208-219 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14253 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images

AU - Syed, Ibrahim Hassan

AU - McKeever, Susan

AU - Feighan, Kieran

AU - Power, David

AU - O’Sullivan, Dympna

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for significant cost reduction in inspections, improved safety conditions during checks, and acceleration of the current manual inspection processes.

AB - Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for significant cost reduction in inspections, improved safety conditions during checks, and acceleration of the current manual inspection processes.

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Syed IH, McKeever S, Feighan K, Power D, O’Sullivan D. A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images. In Christensen HI, Corke P, Detry R, Weibel JB, Vincze M, editors, Computer Vision Systems - 14th International Conference, ICVS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 208-219. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-44137-0_18

A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images

Abstract

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Keywords

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