David Hall

David is a research fellow with the ACRV 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 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 and developing a scene understanding robotic vision challenge.  He now looks forward to solving these problems and giving the world robust and adaptable robotic vision systems.

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