Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance
Several natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, con...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5598690 |
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| author | Ren-Yi Kung Nai-Hsin Pan Charles C.N. Wang Pin-Chan Lee |
| author_facet | Ren-Yi Kung Nai-Hsin Pan Charles C.N. Wang Pin-Chan Lee |
| author_sort | Ren-Yi Kung |
| collection | DOAJ |
| description | Several natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). Based on a pretrained CNN VGG-16 classifier, this model applies class activation mapping for object localization. After identifying its limitations in real-life applications, this study determined the model’s robustness and ability to accurately detect and localize defects in the external wall tiles of buildings. For real-time detection and localization, this study applied this model by using mobile devices and drones. The results show that the application of deep learning with UAV can effectively detect various kinds of external wall defects and improve the detection efficiency. |
| format | Article |
| id | doaj-art-d7efc566ae1841bdb54f65ec105c76e4 |
| institution | DOAJ |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-d7efc566ae1841bdb54f65ec105c76e42025-08-20T03:23:52ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/55986905598690Application of Deep Learning and Unmanned Aerial Vehicle on Building MaintenanceRen-Yi Kung0Nai-Hsin Pan1Charles C.N. Wang2Pin-Chan Lee3Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, TaiwanDepartment of Construction Engineering, National Yunlin University of Science and Technology, Douliu, TaiwanDepartment of Bioinformatics and Medical Engineering, Asia University / Center for Artificial Intelligence and Precision Medicine Research, Asia University, Wufeng, TaiwanYuejin Technology, Ltd., New Taipei City, TaiwanSeveral natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). Based on a pretrained CNN VGG-16 classifier, this model applies class activation mapping for object localization. After identifying its limitations in real-life applications, this study determined the model’s robustness and ability to accurately detect and localize defects in the external wall tiles of buildings. For real-time detection and localization, this study applied this model by using mobile devices and drones. The results show that the application of deep learning with UAV can effectively detect various kinds of external wall defects and improve the detection efficiency.http://dx.doi.org/10.1155/2021/5598690 |
| spellingShingle | Ren-Yi Kung Nai-Hsin Pan Charles C.N. Wang Pin-Chan Lee Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance Advances in Civil Engineering |
| title | Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance |
| title_full | Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance |
| title_fullStr | Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance |
| title_full_unstemmed | Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance |
| title_short | Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance |
| title_sort | application of deep learning and unmanned aerial vehicle on building maintenance |
| url | http://dx.doi.org/10.1155/2021/5598690 |
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