YorkSpace has migrated to a new version of its software. Access our Help Resources to learn how to use the refreshed site. Contact diginit@yorku.ca if you have any questions about the migration.
 

Exploitation of Deep Learning in the Automatic Detection of Cracks on Paved Roads

Simple item page
dc.contributor.advisorHu, Baoxin
dc.contributor.authorJung, Won Mo
dc.date.accessioned2020-11-13T13:49:31Z
dc.date.available2020-11-13T13:49:31Z
dc.date.copyright2020-07
dc.date.issued2020-11-13
dc.date.updated2020-11-13T13:49:31Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractInformation on the severity of pavement cracks is critical for pavement repair services. This study aimed to exploit the applications of deep learning networks to improve the detection and classification of pavement cracks. An improved Convolution Neural Network (CNN) with structured prediction was proposed. Also, Fully Convolutional Network (FCN), U-Net, and attention U-Net were implemented and explored with different optimizers and loss functions. The developed networks were tested on the data collected in Ontario, Canada with the purpose of localizing the cracks and identifying their severity levels based on three categories (low, medium, and high). The results showed that the improved CNN approach performed better than its original version with the F1-score increased from (5.21%, 30.85%, 83.51%) to (19.63%, 55.60%, 85.89%) for the detection of the cracks with the three severities. Furthermore, FCN, U-Net, and attention U-Net achieved slightly better results than the improved CNN approach with the F1-scores of (32.08%, 68.82%, 89.89%), (40.06%, 69.97%, 89.07%), and (40.53%, 71.27%, 89.95%), respectively.
dc.identifier.urihttp://hdl.handle.net/10315/37902
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectEnvironmental science
dc.subject.keywordsCrack Detection
dc.subject.keywordsPavement
dc.subject.keywordsConvolution Neural Network
dc.subject.keywordsCNN
dc.subject.keywordsFully Convolutional Network
dc.subject.keywordsFCN
dc.subject.keywordsPavement distress
dc.subject.keywordsInstance segmentation
dc.subject.keywordsU-Net
dc.subject.keywordsAttention U-Net
dc.subject.keywordsDeep Learning
dc.subject.keywordsMachine Learning
dc.subject.keywordsArtificial Intelligence (AI)
dc.subject.keywordsImage processing
dc.subject.keywordsComputer Vision
dc.titleExploitation of Deep Learning in the Automatic Detection of Cracks on Paved Roads
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Jung_Wonmo_2020_Masters.pdf
Size:
1.71 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.83 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.36 KB
Format:
Plain Text
Description:
Download

Collections

Earth & Space Science

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.