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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2020
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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 |
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Engineering::Electrical and electronic engineering Wang, Shiyong Deep learning for ground penetrating radar image processing |
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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 |
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Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/140154 |
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