Projects
Using AI and cameras to identify and monitor litter, link
The project aimed to address the pressing issue of marine debris by leveraging deep learning techniques to estimate the volume of floating litter in waterways and identify key hotspots. By evaluating existing management systems, the goal was to contribute to the preservation of healthy water ecosystems.
-
Development of Local Machine Web Application for Image Analysis and Visualization.
As part of my role, I spearheaded the creation of a robust web-based application designed to operate exclusively on local machines. Leveraging Flask, I engineered a comprehensive solution for visualizing images, running pre-trained machine learning models for image detection and classification, and presenting data analysis results through intuitive visual interfaces.
-
Glassdoor Employee Sentiment Analysis.
I collaborated with the Department of Business Strategy and Innovation at Griffith Business School on a funded research project analysing changes in employee work preferences during the COVID-19 period using large-scale review data.
-
Predicting Koala Road Crossing Behaviours using AI-Powered Observation Network, link
As a casual researcher, I contributed to a pilot study focused on training artificial intelligence (AI) systems for koala "face recognition" at crossing locations throughout South East Queensland. The project aimed to leverage facial recognition technology to enhance koala conservation efforts and improve safety measures at these critical crossings.
-
An Automated System for the Analysis of Road Safety and Conditions, link
This ARC Linkage project was a collaboration between CQUinversity and DTMR (Department of Transport and Main Roads) in Queensland, Australia. The main aim of this project is to develop an automatic system for the detection of road safety attributes and distances to improve road infrastructure and reduce fatalities on the roads. The deep learning based technique is developed and evaluated using the digital video road data which is collected over every state road in Queensland annually. The system finnaly tested on state-controlled roads in Queensland to assess road safety and conditions automatically. The major challenges of current manual systems are to accurately detect, segment and classify all road objects and also calculate the distance between objects. Deep learning has the ability to address such major challenges.
-
Foreground-Background Classification for Crack Detection, link
This project was a collaboration between School of ICT and school of Civil Engineering at Griffith University in Queensland, Australia. The main aim of this project was to develop an automatic system for the detection of road cracks using deep learning techniques. The system was developed and evaluated using an image dataset created by capturing digital images by mobile camera from asphalt roads and concrete bridges. The system was finally tested on state-controlled roads in Queensland to assess road safety and conditions automatically. The major challenges was the small size of dataset in order to generalise the model. To address this challenge data augmentation techniques have been applied.
-
Designing and developing the algorithm to correct respiratory motion from PET/CT lung cancer images
As a Graduate Research Assistant within the Biomedical Imaging & Signal Processing research group at the School of Electrical and Electronic Engineering, I played an integral role in advancing the field of biomedical imaging. My work primarily focused on exploring innovative techniques for image reconstruction and addressing challenges related to respiratory motion correction in PET/CT imaging systems.
-
Image matching using dimensionally reduced embedded Earth Mover's Distance