Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158075
Title: Machine learning mask R-CNN for GPR B-scans
Authors: Wu, Yi Xuan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: 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
Project: B3113-211
Abstract: 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.
URI: https://hdl.handle.net/10356/158075
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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