Kirk, R., Cielniak, G. and Mangan, M. orcid.org/0000-0002-0293-8874 (2020) L*a*b*fruits : a rapid and robust outdoor fruit detection system combining bio-inspired features with one-stage deep learning networks. Sensors, 20 (1). 275.
Abstract
Automation of agricultural processes requires systems that can accurately detect and classify produce in real industrial environments that include variation in fruit appearance due to illumination, occlusion, seasons, weather conditions, etc. In this paper we combine a visual processing approach inspired by colour-opponent theory in humans with recent advancements in one-stage deep learning networks to accurately, rapidly and robustly detect ripe soft fruits (strawberries) in real industrial settings and using standard (RGB) camera input. The resultant system was tested on an existent data-set captured in controlled conditions as well our new real-world data-set captured on a real strawberry farm over two months. We utilise F1 score, the harmonic mean of precision and recall, to show our system matches the state-of-the-art detection accuracy ( F1 : 0.793 vs. 0.799) in controlled conditions; has greater generalisation and robustness to variation of spatial parameters (camera viewpoint) in the real-world data-set ( F1 : 0.744); and at a fraction of the computational cost allowing classification at almost 30fps. We propose that the L*a*b*Fruits system addresses some of the most pressing limitations of current fruit detection systems and is well-suited to application in areas such as yield forecasting and harvesting. Beyond the target application in agriculture this work also provides a proof-of-principle whereby increased performance is achieved through analysis of the domain data, capturing features at the input level rather than simply increasing model complexity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | fruit detection; deep learning; computer vision; agricultural robotics; multi-modal; strawberry perception; fruit localisation; outdoor detection; bio-inspired; one-stage networks |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Feb 2020 12:11 |
Last Modified: | 11 Feb 2020 12:11 |
Status: | Published |
Publisher: | MDPI |
Refereed: | Yes |
Identification Number: | 10.3390/s20010275 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155409 |