Dr David Hall
Faculty of Engineering,
School of Electrical Engineering & Robotics
Biography
Dr David Hall is a research fellow at QUT whose long-term goal is to see robots able to cope with the unpredictable real world. He began this journey with his PhD on adaptable systems for autonomous weed species recognition as a part of the strategic investment in farm robotics (SIFR) team. Since April 2018 he has worked as part of the robotic vision challenge group within the Australian Centre for 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, he has 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
Personal details
Positions
- Postdoctoral Research Fellow
Faculty of Engineering,
School of Electrical Engineering & Robotics
Qualifications
- PhD (Queensland University of Technology)
- Bachelor of Engineering (Infomechatronics) (Queensland University of Technology)
Teaching
2019 ENB439 - Guest Lecture 23-05-2019
Selected publications
- Hall D, Dayoub F, Perez T, McCool C, (2018) A rapidly deployable classification system using visual data for the application of precision weed management, Computers and Electronics in Agriculture, 148, pp. 107-120.
- Hall D, Dayoub F, Perez T, McCool C, (2017) A transplantable system for weed classification by agricultural robotics, Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), pp. 5174-5179.
- Hall D, Dayoub F, Kulk J, McCool C, (2017) Towards unsupervised weed scouting for agricultural robotics, Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5223-5230.
- Hall D, McCool C, Dayoub F, Suenderhauf N, Upcroft B, (2015) Evaluation of features for leaf classification in challenging conditions, Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision (WACV 2015), pp. 797-804.
QUT ePrints
For more publications by David, explore their research in QUT ePrints (our digital repository).