Strawberry detection using a heterogeneous multi-processor platform

Brandenburg, S, Machado, P ORCID: https://orcid.org/0000-0003-1760-3871, Lama, N ORCID: https://orcid.org/0000-0002-7737-8664 and Mcginnity, TM ORCID: https://orcid.org/0000-0002-9897-4748, 2020. Strawberry detection using a heterogeneous multi-processor platform. In: Conference proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Workshop on Perception, Planning and Mobility in Forestry Robotics (WPPMFR 2020). [United States]: IEEE.

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Abstract

Over the last few years, the number of precision farming projects has increased specifically in harvesting robots and many of which have made continued progress from identifying crops to grasping the desired fruit or vegetable. One of the most common issues found in precision farming projects is that successful application is heavily dependent not just on identifying the fruit but also on ensuring that localisation allows for accurate navigation. These issues become significant factors when the robot is not operating in a prearranged environment, or when vegetation becomes too thick, thus covering crop. Moreover, running a state-of-the-art deep learning algorithm on an embedded platform is also very challenging, resulting most of the times in low frame rates. This paper proposes using the You Only Look Once version 3 (YOLOv3) Convo-lutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3% over the test set comprised of 146 images.

Item Type:	Chapter in book
Creators:	Brandenburg, S., Machado, P., Lama, N. and Mcginnity, T.M.
Publisher:	IEEE
Place of Publication:	[United States]
Date:	25 October 2020
Identifiers:	Number Type 1386737 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	28 Jul 2022 09:06
Last Modified:	28 Jul 2022 09:06
URI:	https://irep.ntu.ac.uk/id/eprint/46761

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