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...

Full description

Saved in:

Bibliographic Details
Main Author:	Wu, Yi Xuan
Other Authors:	Abdulkadir C. Yucel
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering
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

Machine learning mask R-CNN for GPR B-scans

Similar Items