Version 1
: Received: 9 May 2023 / Approved: 10 May 2023 / Online: 10 May 2023 (07:58:06 CEST)
How to cite:
Eldor, I.; Lee, J.; Gil, H.; Lee, J.H. A Vision-Based System for Inspection of Expansion Joints in Concrete Pavement. Preprints2023, 2023050701. https://doi.org/10.20944/preprints202305.0701.v1
Eldor, I.; Lee, J.; Gil, H.; Lee, J.H. A Vision-Based System for Inspection of Expansion Joints in Concrete Pavement. Preprints 2023, 2023050701. https://doi.org/10.20944/preprints202305.0701.v1
Eldor, I.; Lee, J.; Gil, H.; Lee, J.H. A Vision-Based System for Inspection of Expansion Joints in Concrete Pavement. Preprints2023, 2023050701. https://doi.org/10.20944/preprints202305.0701.v1
APA Style
Eldor, I., Lee, J., Gil, H., & Lee, J.H. (2023). A Vision-Based System for Inspection of Expansion Joints in Concrete Pavement. Preprints. https://doi.org/10.20944/preprints202305.0701.v1
Chicago/Turabian Style
Eldor, I., Heungbae Gil and Jung Hee Lee. 2023 "A Vision-Based System for Inspection of Expansion Joints in Concrete Pavement" Preprints. https://doi.org/10.20944/preprints202305.0701.v1
Abstract
The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
