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Adaptive Image Thresholding of Yellow Peppers for a Harvesting Robot
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0003-0830-5303
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-4600-8652
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-7242-2200
2018 (English)In: Robotics, E-ISSN 2218-6581, Vol. 7, no 1, article id 11Article in journal (Refereed) Published
Abstract [en]

The presented work is part of the H2020 project SWEEPER with the overall goal to develop a sweet pepper harvesting robot for use in greenhouses. As part of the solution, visual servoing is used to direct the manipulator towards the fruit. This requires accurate and stable fruit detection based on video images. To segment an image into background and foreground, thresholding techniques are commonly used. The varying illumination conditions in the unstructured greenhouse environment often cause shadows and overexposure. Furthermore, the color of the fruits to be harvested varies over the season. All this makes it sub-optimal to use fixed pre-selected thresholds. In this paper we suggest an adaptive image-dependent thresholding method. A variant of reinforcement learning (RL) is used with a reward function that computes the similarity between the segmented image and the labeled image to give feedback for action selection. The RL-based approach requires less computational resources than exhaustive search, which is used as a benchmark, and results in higher performance compared to a Lipschitzian based optimization approach. The proposed method also requires fewer labeled images compared to other methods. Several exploration-exploitation strategies are compared, and the results indicate that the Decaying Epsilon-Greedy algorithm gives highest performance for this task. The highest performance with the Epsilon-Greedy algorithm ( ϵ = 0.7) reached 87% of the performance achieved by exhaustive search, with 50% fewer iterations than the benchmark. The performance increased to 91.5% using Decaying Epsilon-Greedy algorithm, with 73% less number of iterations than the benchmark.

Place, publisher, year, edition, pages
MDPI , 2018. Vol. 7, no 1, article id 11
Keyword [en]
reinforcement learning, Q-Learning, image thresholding, ϵ-greedy strategies
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:umu:diva-144513DOI: 10.3390/robotics7010011OAI: oai:DiVA.org:umu-144513DiVA: diva2:1180297
Funder
EU, Horizon 2020, 644313
Available from: 2018-02-05 Created: 2018-02-05 Last updated: 2018-02-06Bibliographically approved

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Ostovar, AhmadRingdahl, OlaHellström, Thomas
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