Master of Philosophy
School of Electrical, Computer and Telecommunications Engineering
Biosecurity scanning plays a crucial role in preventing exotic pests, weeds and contaminants from entering a country through shipping containers. Exposure to biosecurity risks causes a substantial loss to the native environment, production value and public health. Currently, these threats are managed via manual inspection, detector dogs and x-ray scanners; however, these procedures are time-consuming, error-prone, or costly.
In this research, we propose a novel approach for biosecurity risk detection that utilizes hyperspectral imaging technology and semantic image segmentation. This approach segments the target objects in a hyperspectral image by analyzing their spatial and spectral signatures. The target objects in this project include metal, plants, soil, creatures and background.
Bui, Ly, Deep Learning for Hyperspectral Image Segmentation in Biosecurity Scanning, Master of Philosophy thesis, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, 2021. https://ro.uow.edu.au/theses1/1217
FoR codes (2008)
0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 0906 ELECTRICAL AND ELECTRONIC ENGINEERING
Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.