Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun

Goh, Wei Sheng and Wan Din, Wan Isni Sofiah and Waseem, Quadri and Zabidi, A. (2023) Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun. International Journal of Software Engineering & Computer Sciences (IJSECS), 9 (1). pp. 10-26. ISSN 2289-8522. (Published)

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Crack detection in old buildings has been shown to be inefficient, with many technical challenges such as physical inspection and difficult measurements. It is important to have an automatic, fast visual inspection of these building components to detect cracks by evaluating their conditions (impact) and the level of their risk. Unmanned Aerial Vehicles (UAV) can automate, avoid visual inspection, and avoid other physical check-ups of these buildings. Automated crack detection using Machine Learning Algorithms (MLA), especially a Conventional Neural Network (CNN), along with an Unmanned Aerial Vehicle (UAV), can be effective and both can efficiently work together to detect the cracks in buildings using image processing techniques. The purpose of this research project is to evaluate currently available crack detection systems and to develop an automated crack detection system using Aggregate Channel Features (ACF) that can be used with unmanned aerial vehicles (UAV). Therefore, we conducted a real-world experiment of crack detection at Hospital Raja Permaisuri Bainun using DJI Mavic Air (Drone Hardware) and DJI GO 4(Drone Software) using CNN through MATLAB software with CNN-SVM method with the accuracy rate of 3.0 percent increased from 82.94% to 85.94%. in comparison with other ML algorithms like CNN Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN).

Item Type: Article
Uncontrolled Keywords: Crack Detection, Structures, Unmanned Aerial Vehicle, Machine Learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 03 May 2023 01:08
Last Modified: 03 May 2023 01:08
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