Dr David Hall

Postdoctoral Research Fellow

General Information

Affiliations:

    • Australian Centre for Robotic Vision (ACRV)

    • Queensland University of Technology

Fields of Expertise:

    • Robotic Vision

    • Machine Learning

    • Evaluation Measures

    • Clustering

    • Plant Recognition

Staff Profile: profile

Arxiv Profile: arxiv home

Githib Page: https://github.com/david2611

Contact: d20.hall@qut.edu.au

Hi, I am David Hall, research fellow at the Queensland University of Technology (QUT) whose long-term goal is to see robots able to cope with the unpredictable real world.

I began this journey with my PhD on adaptable systems for autonomous weed species recognition as a part of the strategic investment in farm robotics (SIFR) team. Since April 2018 I have worked as part of the robotic vision challenge group within the Australian Centre of Robotic Vision (ACRV) and QUT Centre for Robotics designing challenges, benchmarks, and evaluation measures that assist emerging areas of robotic vision research.

As a part of the robotic vision challenge group, I have assisted in:

  • Defining the field of probabilistic object detection (PrOD)

  • Creating the probability-based detection quality (PDQ) evaluation measure

  • Developing a PrOD robotic vision challenge

  • Developing a scene understanding robotic vision challenge

Now that I have spent some time developing robotic vision challenges, I look forward to solving these problems and giving the world robust and adaptable robotic vision systems, whilst also keeping a keen eye on the progress of robotic vision analysis.

Recent 1st Author Publications

This is a list of my recent published papers (not arxiv) where I was 1st author on the paper. For a complete list of all papers I have assisted in publishing please go to my "All Publications" page.

Title: Probabilstic Object Detection: Definition and Evaluation

Authors: David Hall, Feras Dayoub, John Skinner, Haoyang Zhang, Dimity Miller, Peter Corke, Gustavo Carneiro, Anelia Angelova, Niko Suenderhauf

Year: 2020

Published in: 2020 IEEE Winter Conference on Applications of Computer Vision

Manuscript Download Link: WACV2020-arxiv

Bibtex: prod2020.bib


Probability-based Detection Quality

Thesis

Title: A rapidly deployable approach for automated visual weed classification without prior species knowledge

Authors: David Hall

Year: 2018

Published by: Queensland University of Technology

Link to Paper: thesis2018.pdf

Bibtex: thesis.bib

Title: A rapidly deployable classification system using visual data for the application of precision weed management.

Authors: David Hall, Feras Dayoub, Tristan Perez, Chris McCool

Year: 2018

Published in: Computers and Electronics in Agriculture, Vol. 148

Manuscript Download Link: COMPAG2018_manuscript.pdf

Official Paper Link:

https://www.sciencedirect.com/science/article/pii/S0168169917309018

Bibtex: compag2018.bib


Title: A Transplantable System for Weed Classification by Agricultural Robotics

Authors: David Hall, Feras Dayoub, Tristan Perez, Chris McCool

Year: 2017

Published in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems

Paper Link: IROS2017.pdf

Bibtex: iros2017.bib


Title: Towards Unsupervised Weed Scouting for Agricultural Robotics

Authors: David Hall, Feras Dayoub, Jason Kulk, Chris McCool

Year: 2017

Published in: IEEE International Conference on Robotics and Automation 2017

Paper Link: ICRA2017.pdf

Bibtex: icra2017.bib

Title: Evaluation of Features for Leaf Classification in Challenging Conditions

Authors: David Hall, Chris McCool, Feras Dayoub, Niko Sünderhauf, Ben Upcroft

Year: 2015

Published in: 2015 IEEE Winter Conference on Applications of Computer Vision

Paper Link: WACV2015.pdf

Bibtex: wacv2015.bib