Machine learning mask R-CNN for GPR B-scans
Ground Penetrating Radar (GPR) is a commonly used technology to detect underground objects and environments for different purposes. The output generated from moving GPR across the ground surface is called the GPR scan. Realistic subsurface surroundings can be visualized, mapped, and monitored...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/158075 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-158075 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1580752023-07-07T19:05:18Z Machine learning mask R-CNN for GPR B-scans Wu, Yi Xuan 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 Ground Penetrating Radar (GPR) is a commonly used technology to detect underground objects and environments for different purposes. The output generated from moving GPR across the ground surface is called the GPR scan. Realistic subsurface surroundings can be visualized, mapped, and monitored with these data. In B-scans, underground objects are represented in hyperbolic signatures. However, the process of manual recognition of hyperbolas presented in the B-scan is difficult and tedious due to the noisy and complex nature of subterranean environments. Mask R-CNN will be implemented to perform object detection and instance segmentation with supervised learning. The procedures include data preparation, training, testing and evaluation of prediction results. Bachelor of Engineering (Information Engineering and Media) 2022-05-29T06:52:38Z 2022-05-29T06:52:38Z 2022 Final Year Project (FYP) Wu, Y. X. (2022). Machine learning mask R-CNN for GPR B-scans. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158075 https://hdl.handle.net/10356/158075 en B3113-211 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 Wu, Yi Xuan Machine learning mask R-CNN for GPR B-scans |
description |
Ground Penetrating Radar (GPR) is a commonly used technology to detect underground
objects and environments for different purposes. The output generated from moving
GPR across the ground surface is called the GPR scan. Realistic
subsurface surroundings can be visualized, mapped, and monitored with these data.
In B-scans, underground objects are represented in hyperbolic signatures. However,
the process of manual recognition of hyperbolas presented in the B-scan is difficult
and tedious due to the noisy and complex nature of subterranean environments. Mask R-CNN will be implemented to perform object detection and instance segmentation with supervised learning. The procedures include data preparation, training, testing and evaluation of prediction results. |
author2 |
Abdulkadir C. Yucel |
author_facet |
Abdulkadir C. Yucel Wu, Yi Xuan |
format |
Final Year Project |
author |
Wu, Yi Xuan |
author_sort |
Wu, Yi Xuan |
title |
Machine learning mask R-CNN for GPR B-scans |
title_short |
Machine learning mask R-CNN for GPR B-scans |
title_full |
Machine learning mask R-CNN for GPR B-scans |
title_fullStr |
Machine learning mask R-CNN for GPR B-scans |
title_full_unstemmed |
Machine learning mask R-CNN for GPR B-scans |
title_sort |
machine learning mask r-cnn for gpr b-scans |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/158075 |
_version_ |
1772826572676399104 |