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L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks

1
Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
2
Sheffield Robotics, School of Computer Science, University of Sheffield, Sheffield S10 2TN, UK
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(1), 275; https://doi.org/10.3390/s20010275
Received: 18 November 2019 / Revised: 19 December 2019 / Accepted: 30 December 2019 / Published: 3 January 2020
(This article belongs to the Section Physical Sensors)
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 F 1 score, the harmonic mean of precision and recall, to show our system matches the state-of-the-art detection accuracy ( F 1 : 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 ( F 1 : 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. View Full-Text
Keywords: fruit detection; deep learning; computer vision; agricultural robotics; multi-modal; strawberry perception; fruit localisation; outdoor detection; bio-inspired; one-stage networks fruit detection; deep learning; computer vision; agricultural robotics; multi-modal; strawberry perception; fruit localisation; outdoor detection; bio-inspired; one-stage networks
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Kirk, R.; Cielniak, G.; Mangan, M. L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks. Sensors 2020, 20, 275.

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