Evaluations of deep learning methods for detection of gap in concrete structures
This dissertation applies 3 networks(FRCNN,Yolov3,Yolov4) to solve gap detection on a very small data set and make brief evaluations about their structures and performances.
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التنسيق: | Thesis-Master by Coursework |
اللغة: | English |
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Nanyang Technological University
2021
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الوصول للمادة أونلاين: | https://hdl.handle.net/10356/153127 |
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sg-ntu-dr.10356-1531272023-07-04T17:39:56Z Evaluations of deep learning methods for detection of gap in concrete structures Yang, Zhen Cheah Chien Chern School of Electrical and Electronic Engineering ECCCheah@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence This dissertation applies 3 networks(FRCNN,Yolov3,Yolov4) to solve gap detection on a very small data set and make brief evaluations about their structures and performances. Master of Science (Computer Control and Automation) 2021-11-08T01:10:27Z 2021-11-08T01:10:27Z 2021 Thesis-Master by Coursework Yang, Z. (2021). Evaluations of deep learning methods for detection of gap in concrete structures. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153127 https://hdl.handle.net/10356/153127 en application/pdf Nanyang Technological University |
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Singapore Singapore |
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Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
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Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Yang, Zhen Evaluations of deep learning methods for detection of gap in concrete structures |
description |
This dissertation applies 3 networks(FRCNN,Yolov3,Yolov4) to solve gap detection on a very small data set and make brief evaluations about their structures and performances. |
author2 |
Cheah Chien Chern |
author_facet |
Cheah Chien Chern Yang, Zhen |
format |
Thesis-Master by Coursework |
author |
Yang, Zhen |
author_sort |
Yang, Zhen |
title |
Evaluations of deep learning methods for detection of gap in concrete structures |
title_short |
Evaluations of deep learning methods for detection of gap in concrete structures |
title_full |
Evaluations of deep learning methods for detection of gap in concrete structures |
title_fullStr |
Evaluations of deep learning methods for detection of gap in concrete structures |
title_full_unstemmed |
Evaluations of deep learning methods for detection of gap in concrete structures |
title_sort |
evaluations of deep learning methods for detection of gap in concrete structures |
publisher |
Nanyang Technological University |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/153127 |
_version_ |
1772827788561088512 |