Predictive Maintenance of Pavement Cracks in Airport Facilities Based on Drones and Computer Vision
Keywords:
Digital Twin, Deep Learning, Airport Facility, Crack, Artificial Intelligence, Machine LearningSynopsis
This is a Chapter in:
Book:
Intelligent and Sustainable Solutions
Print ISBN 978-1-6692-0012-3
Online ISBN 978-1-6692-0011-6
Series:
Chronicle of Computing
Chapter Abstract [Under Revision]:
Manual inspection of damages in airport facilities such as pavement cracks is difficult due to the reliability objective and the high demands of time and costs. This can be automated using a system of unmanned aerial vehicles (UAVs) for aerial imagery of damages. Many computer vision-based approaches have been applied by several researchers to address the limitations of crack detection but they have their limitations. The purpose of this paper is to describe how the limitations can be overcome by using various hybrid methods based on artificial intelligence (AI) and deep learning (DL) techniques. Also, it shows how convolutional neural networks may be introduced in drones to automate the detection of pavement cracks in airport facilities. The outline of the proposed system is composed of three modules which are: Image acquisition, Crack detection, and Image-based 3D modeling. In addition, this paper has shown that this proposed system can participate in building a 3D digital representation of pavement cracks in airport facilities automatically from a DSLR camera within the context of the digital twin.
About this Paper
Cite this paper as:
Lahna T.(2025). Predictive Maintenance of Pavement Cracks in Airport Facilities Based on Drones and Computer Vision. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#7. https://doi.org/10.55432/978-1-6692-0011-6_5
Presented at:
The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025
Contact:
Tarik Lahna
lahnatk@ucla.edu
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