A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms
Perez, Tristan, Sa, Inkyu, McCool, Christopher, & Lehnert, Christopher (2015) A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms. In ICRA 2015 : IEEE International Conference on Robotics and Automation, 26 -30th May 2015, Seattle, Washington.
This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).
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|Item Type:||Conference Paper|
|Divisions:||Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 The Authors|
|Deposited On:||16 Nov 2015 01:48|
|Last Modified:||18 Nov 2015 05:12|
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