Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks
Joint Authors
Nguyen, Quoc-Lam
Hoang, Nhat-Duc
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-24
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Effective road maintenance requires adequate periodic surveys of asphalt pavement condition.
The manual process of pavement assessment is labor intensive and time-consuming.
This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning.
Advanced image processing techniques including fast local Laplacian filter, Sobel filter, steerable filter, and projection integral are employed for image enhancement and analysis to extract useful features from digital images.
Based on the features produced by these image processing techniques, adaptive boosting classification tree is used to perform pavement crack recognition tasks.
A dataset of image samples consisting of five classes (alligator crack, diagonal crack, longitudinal crack, noncrack, and transverse crack) has been collected to construct and verify the performance of the adaptive boosting classification tree.
The experimental results show that the proposed approach has achieved a high crack classification accuracy which is roughly 90%.
Therefore, the newly developed model is a promising alternative to help transportation agencies in pavement condition evaluation.
American Psychological Association (APA)
Hoang, Nhat-Duc& Nguyen, Quoc-Lam. 2018. Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks. Advances in Civil Engineering،Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1116310
Modern Language Association (MLA)
Hoang, Nhat-Duc& Nguyen, Quoc-Lam. Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks. Advances in Civil Engineering No. 2018 (2018), pp.1-17.
https://search.emarefa.net/detail/BIM-1116310
American Medical Association (AMA)
Hoang, Nhat-Duc& Nguyen, Quoc-Lam. Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks. Advances in Civil Engineering. 2018. Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1116310
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1116310