On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture
Sa, Inkyu, McCool, Christopher, Lehnert, Christopher, & Perez, Tristan (2015) On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture. In ICRA 2015 : IEEE International Conference on Robotics and Automation, 26 -30th May 2015, Seattle, Washington.
Developing accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information.
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|Item Type:||Conference Item (Poster)|
|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 04:41|
|Last Modified:||17 Nov 2015 06:29|
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