- Dr David Hall
- Postdoctoral Research Fellow
Science and Engineering Faculty,
Electrical Engineering, Computer Science,
Centre of Excellence (COE) in Robotic Vision
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PhD (Queensland University of Technology), Bachelor of Engineering (Infomechatronics) (Queensland University of Technology)
Dr David Hall is a postdoctoral research fellow at the Australian Centre for Robotic Vision (ACRV) – Queensland University of Technology (QUT). He attained his PhD in 2018 at QUT in the field of agricultural robotics focusing on automated weed species recognition. His research interests are on ensuring that vision and machine learning systems developed for the real-world are adaptable enough to cope with the cluttered, varied, and changing world around them. His PhD focused on how weed species recognition can be rapidly deployed to fields without knowledge of the weed species in advance. He currently works with the ACRV on developing benchmarks for robotic vision, developing challenges and evaluation measures to encourage the computer vision and robotics research communities to develop probabilistic and adaptable recognition systems.
ENB439 – Guest Lecture 23-05-2019
- Hall DR, Dayoub F, Perez T, McCool CS, (2018) A rapidly deployable classification system using visual data for the application of precision weed management, Computers and Electronics in Agriculture p107-120
- Hall DR, Dayoub F, Perez T, McCool CS, (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) p5174-5179
- Hall DR, Dayoub F, Kulk J, McCool CS, (2017) Towards unsupervised weed scouting for agricultural robotics, Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA) p5223-5230
- Hall DR, McCool CS, 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) p797-804
For more publications by this staff member, visit QUT ePrints, the University's research repository.