Visual detection of occluded crop: For automated harvesting

McCool, Christopher, Sa, Inkyu, Dayoub, Feras, Lehnert, Christopher, Perez, Tristan, & Upcroft, Ben (2016) Visual detection of occluded crop: For automated harvesting. In IEEE International Conference on Robotics and Automation (ICRA 2016), 16-21 May 2016, Stockholm, Sweden.

Abstract

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Impact and interest:

1 citations in Scopus
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ID Code: 94274
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: pattern recognition, crop detection
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2016 [Please consult the author]
Deposited On: 03 Apr 2016 23:45
Last Modified: 20 Jun 2016 04:20

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