A YOLOv4 based defect detector for building facade inspection
The building facade defect inspection nowadays is mainly conducted by manpower, which is costly and inefficient. It is also dangerous when the surveyors work at high levels of building. Some defects are unnoticeable by naked eyes. This may lead to potential risks to users of building. In this projec...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/149890 |
| _version_ | 1811681028907991040 |
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| author | Wang, Hanyu |
| author2 | Xie Lihua |
| author_facet | Xie Lihua Wang, Hanyu |
| author_sort | Wang, Hanyu |
| collection | NTU |
| description | The building facade defect inspection nowadays is mainly conducted by manpower, which is costly and inefficient. It is also dangerous when the surveyors work at high levels of building. Some defects are unnoticeable by naked eyes. This may lead to potential risks to users of building. In this project, a building facade detector is implemented based on deep learning techniques. It aims to detect various categories of defects on building facade. The detector is implemented using YOLOv4 network. The well-trained detector reaches 50% overall performance on detecting 9 classes of defects and 3 other objects on building facade. The result proves the feasibility of deploying the detector model on UAV to conduct near real-time building facade defect detection, and the practicability of applying similar methods on other defect detection works. |
| first_indexed | 2024-10-01T03:34:26Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149890 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:34:26Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1498902023-07-07T18:03:19Z A YOLOv4 based defect detector for building facade inspection Wang, Hanyu Xie Lihua School of Electrical and Electronic Engineering ELHXIE@ntu.edu.sg Engineering::Electrical and electronic engineering The building facade defect inspection nowadays is mainly conducted by manpower, which is costly and inefficient. It is also dangerous when the surveyors work at high levels of building. Some defects are unnoticeable by naked eyes. This may lead to potential risks to users of building. In this project, a building facade detector is implemented based on deep learning techniques. It aims to detect various categories of defects on building facade. The detector is implemented using YOLOv4 network. The well-trained detector reaches 50% overall performance on detecting 9 classes of defects and 3 other objects on building facade. The result proves the feasibility of deploying the detector model on UAV to conduct near real-time building facade defect detection, and the practicability of applying similar methods on other defect detection works. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-06-10T02:18:32Z 2021-06-10T02:18:32Z 2021 Final Year Project (FYP) Wang, H. (2021). A YOLOv4 based defect detector for building facade inspection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149890 https://hdl.handle.net/10356/149890 en A1185-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Wang, Hanyu A YOLOv4 based defect detector for building facade inspection |
| title | A YOLOv4 based defect detector for building facade inspection |
| title_full | A YOLOv4 based defect detector for building facade inspection |
| title_fullStr | A YOLOv4 based defect detector for building facade inspection |
| title_full_unstemmed | A YOLOv4 based defect detector for building facade inspection |
| title_short | A YOLOv4 based defect detector for building facade inspection |
| title_sort | yolov4 based defect detector for building facade inspection |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/149890 |
| work_keys_str_mv | AT wanghanyu ayolov4baseddefectdetectorforbuildingfacadeinspection AT wanghanyu yolov4baseddefectdetectorforbuildingfacadeinspection |