Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun
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)...
| المؤلفون الرئيسيون: | , , , |
|---|---|
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Penerbit UMP
2023
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://umpir.ump.edu.my/id/eprint/37531/1/Investigation%20and%20Analysis%20of%20Crack%20Detection%20using%20UAV%20and%20CNN.pdf |
| _version_ | 1825814948813471744 |
|---|---|
| author | Goh, Wei Sheng Wan Din, Wan Isni Sofiah Waseem, Quadri Zabidi, A. |
| author_facet | Goh, Wei Sheng Wan Din, Wan Isni Sofiah Waseem, Quadri Zabidi, A. |
| author_sort | Goh, Wei Sheng |
| collection | UMP |
| description | 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). |
| first_indexed | 2024-03-06T13:06:09Z |
| format | Article |
| id | UMPir37531 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:06:09Z |
| publishDate | 2023 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir375312023-05-03T01:08:52Z http://umpir.ump.edu.my/id/eprint/37531/ Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun Goh, Wei Sheng Wan Din, Wan Isni Sofiah Waseem, Quadri Zabidi, A. QA76 Computer software 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). Penerbit UMP 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37531/1/Investigation%20and%20Analysis%20of%20Crack%20Detection%20using%20UAV%20and%20CNN.pdf 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) https://doi.org/10.15282/ijsecs.9.1.2023.2.0106 https://doi.org/10.15282/ijsecs.9.1.2023.2.0106 |
| spellingShingle | QA76 Computer software Goh, Wei Sheng Wan Din, Wan Isni Sofiah Waseem, Quadri Zabidi, A. Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun |
| title | Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun |
| title_full | Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun |
| title_fullStr | Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun |
| title_full_unstemmed | Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun |
| title_short | Investigation and analysis of crack detection using UAV and CNN: A case study of Hospital Raja Permaisuri Bainun |
| title_sort | investigation and analysis of crack detection using uav and cnn a case study of hospital raja permaisuri bainun |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/37531/1/Investigation%20and%20Analysis%20of%20Crack%20Detection%20using%20UAV%20and%20CNN.pdf |
| work_keys_str_mv | AT gohweisheng investigationandanalysisofcrackdetectionusinguavandcnnacasestudyofhospitalrajapermaisuribainun AT wandinwanisnisofiah investigationandanalysisofcrackdetectionusinguavandcnnacasestudyofhospitalrajapermaisuribainun AT waseemquadri investigationandanalysisofcrackdetectionusinguavandcnnacasestudyofhospitalrajapermaisuribainun AT zabidia investigationandanalysisofcrackdetectionusinguavandcnnacasestudyofhospitalrajapermaisuribainun |