Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Gopalakrishnan, Kasthurirangan

doi:10.3390/data3030028

Cited by 210Infrastructure Maintenance and MonitoringAdvanced Neural Network ApplicationsAsphalt Pavement Performance Evaluation

Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

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Gopalakrishnan, Kasthurirangan·2018·Data

DOI 10.3390/data3030028

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Abstract

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

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In [21], the review concluded that both CNN and DCNN perform very well in pavement image classification.
“In the review's conclusion, it is mentioned that both CNN and DCNN perform very well in pavement image classification.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [21]
The review in [21] summarizes 12 of the most recent studies in pavement distress detection that are based on CNN and DCNN methods.
“The review summarizes 12 of the most recent studies in the pavement distress detection which are based on CNN and DCNN methods.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [21]
[21] concludes that CNNs show the best results in terms of pavement image classification in most studies.
“the author concludes that CNNs show the best results in terms of pavement image classification in most studies.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [21]
[21] finds no clear indication that DCNNs can be more accurate than shallower CNNs, and that other parameters such as data pre-processing, augmentation, and feature selection affect classification results.
“There is no clear indication that DCNNs can be more accurate than shallower CNNs and there are a lot of other parameters that affect the classification results, such as data pre-processing, augmentation, and feature selection.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [21]
[21] conducted a detailed review of deep learning techniques applied for pavement distresses detection.
“The author in [21] has conducted a detailed review of deep learning techniques applied for pavement distresses detection.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [21]

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Deep Learning in Pavement Image Analysis and Automated Distress Detection: A Review
Gopalakrishnan, K. (2018) · doi:10.13140/rg.2.2.35354.54728 · Cited by 1 · see citing works
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Authors: Gopalakrishnan, K.
Affiliations: Iowa State University (US)
Corresponding author: Gopalakrishnan, Kasthurirangan
ORCID: Gopalakrishnan, Kasthurirangan
Venue: Data
Publisher: MDPI AG
Published: 2018-07-24
Volume: 3
Issue: 3
Pages: 28
ISSN: 2306-5729 (ISSN-L)
DOI: 10.3390/data3030028
OpenAlex: W2884143694
Cited by: 210
References: 44
Citations by year: 2026: 24 · 2025: 31 · 2024: 40 · 2023: 22 · 2022: 33 · 2021: 26 · 2020: 22 · 2019: 12
FWCI: 13.49
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Data (publishedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Civil and Structural Engineering › Asphalt Pavement Performance Evaluation
Keywords: pavement distress detection, deep convolutional neural networks, crack detection, automated pavement inspection, smart pavements, asset management systems

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Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

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