| Probabilistic Object Detection: Definition and Evaluation D Hall, F Dayoub, J Skinner, H Zhang, D Miller, P Corke, G Carneiro, ... arXiv preprint arXiv:1811.10800, 2018 | 149 | 2018 |
| Evaluation of features for leaf classification in challenging conditions D Hall, C McCool, F Dayoub, N Sunderhauf, B Upcroft 2015 IEEE Winter Conference on Applications of Computer Vision, 797-804, 2015 | 148 | 2015 |
| Retrospectives on the embodied ai workshop M Deitke, D Batra, Y Bisk, T Campari, AX Chang, DS Chaplot, C Chen, ... arXiv preprint arXiv:2210.06849, 2022 | 50 | 2022 |
| Towards unsupervised weed scouting for agricultural robotics D Hall, F Dayoub, J Kulk, C McCool 2017 IEEE International Conference on Robotics and Automation (ICRA), 5223-5230, 2017 | 36 | 2017 |
| Benchmarking Sampling-based Probabilistic Object Detectors D Miller, N Sünderhauf, H Zhang, D Hall, F Dayoub Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 32 | 2019 |
| A rapidly deployable classification system using visual data for the application of precision weed management D Hall, F Dayoub, T Perez, C McCool Computers and Electronics in Agriculture 148, 107-120, 2018 | 29 | 2018 |
| The Robotic Vision Scene Understanding Challenge D Hall, B Talbot, SR Bista, H Zhang, R Smith, F Dayoub, N Sünderhauf arXiv preprint arXiv:2009.05246, 2020 | 17 | 2020 |
| BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots B Talbot, D Hall, H Zhang, SR Bista, R Smith, F Dayoub, N Sünderhauf arXiv preprint arXiv:2008.00635, 2020 | 13 | 2020 |
| What can robotics research learn from computer vision research? P Corke, F Dayoub, D Hall, J Skinner, N Sünderhauf The International Symposium of Robotics Research, 987-1003, 2019 | 12 | 2019 |
| Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM SR Bista, D Hall, B Talbot, H Zhang, F Dayoub, N Sünderhauf 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 10 | 2021 |
| The Probabilistic Object Detection Challenge J Skinner, D Hall, H Zhang, F Dayoub, N Sünderhauf arXiv preprint arXiv:1903.07840, 2019 | 10 | 2019 |
| BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research D Hall, B Talbot, SR Bista, H Zhang, R Smith, F Dayoub, N Sünderhauf The International Journal of Robotics Research 41 (3), 259-269, 2022 | 6 | 2022 |
| A probabilistic challenge for object detection N Sünderhauf, F Dayoub, D Hall, J Skinner, H Zhang, G Carneiro, ... Nature Machine Intelligence 1 (9), 443-443, 2019 | 6 | 2019 |
| A transplantable system for weed classification by agricultural robotics D Hall, F Dayoub, T Perez, C McCool Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International …, 2017 | 5 | 2017 |
| A rapidly deployable approach for automated visual weed classification without prior species knowledge DR Hall PhD thesis, Queensland University of Technology, Brisbane, 2018 | 3 | 2018 |
| Probabilistic Object Detection: Definition and Evaluation-supplementary material D Hall, F Dayoub, J Skinner, H Zhang, D Miller, P Corke, G Carneiro, ... | | |
Feras DayoubThe University of Adelaide - Australian Institute for Machine Learning (AIML)Verified email at adelaide.edu.au
