Deep learning for ground penetrating radar image processing

This project focus on finding the relationship between 2D B-scan images and 3D B-scan images generated by gprMax for convolutional neural network training purpose in object detection and classification. Cylinders with different material and orientations have also been discussed to find the effects o...

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Main Author: Wang, Shiyong
Other Authors: Abdulkadir C. Yucel
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/140154
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1401542023-07-07T18:49:15Z Deep learning for ground penetrating radar image processing Wang, Shiyong Abdulkadir C. Yucel Lee Yee Hui School of Electrical and Electronic Engineering EYHLee@ntu.edu.sg, acyucel@ntu.edu.sg Engineering::Electrical and electronic engineering This project focus on finding the relationship between 2D B-scan images and 3D B-scan images generated by gprMax for convolutional neural network training purpose in object detection and classification. Cylinders with different material and orientations have also been discussed to find the effects on the accuracy of 2D B-scan modelling. Results show that 2D B-scan images are very similar to that of 3D B-scan images of cylinders. In terms of shape and relative position of hyperbola, 2D B-scan images are exactly the same as 3D B-scan images. The minor difference in colour intensity can be negligible. The change in orientation of cylinders can also be represented in 2D B-scan images. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-27T02:48:35Z 2020-05-27T02:48:35Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140154 en B3117-191 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Wang, Shiyong
Deep learning for ground penetrating radar image processing
description This project focus on finding the relationship between 2D B-scan images and 3D B-scan images generated by gprMax for convolutional neural network training purpose in object detection and classification. Cylinders with different material and orientations have also been discussed to find the effects on the accuracy of 2D B-scan modelling. Results show that 2D B-scan images are very similar to that of 3D B-scan images of cylinders. In terms of shape and relative position of hyperbola, 2D B-scan images are exactly the same as 3D B-scan images. The minor difference in colour intensity can be negligible. The change in orientation of cylinders can also be represented in 2D B-scan images.
author2 Abdulkadir C. Yucel
author_facet Abdulkadir C. Yucel
Wang, Shiyong
format Final Year Project
author Wang, Shiyong
author_sort Wang, Shiyong
title Deep learning for ground penetrating radar image processing
title_short Deep learning for ground penetrating radar image processing
title_full Deep learning for ground penetrating radar image processing
title_fullStr Deep learning for ground penetrating radar image processing
title_full_unstemmed Deep learning for ground penetrating radar image processing
title_sort deep learning for ground penetrating radar image processing
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/140154
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