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Controlled Lighting and Illumination-Independent Target Detection for Real-Time Cost-Efficient Applications. The Case Study of Sweet Pepper Robotic Harvesting
Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID iD: 0000-0003-0855-1387
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID iD: 0000-0003-4685-379x
Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 6, article id 1390Article in journal (Refereed) Published
Abstract [en]

Current harvesting robots are limited by low detection rates due to the unstructured and dynamic nature of both the objects and the environment. State-of-the-art algorithms include color- and texture-based detection, which are highly sensitive to the illumination conditions. Deep learning algorithms promise robustness at the cost of significant computational resources and the requirement for intensive databases. In this paper we present a Flash-No-Flash (FNF) controlled illumination acquisition protocol that frees the system from most ambient illumination effects and facilitates robust target detection while using only modest computational resources and no supervised training. The approach relies on the simultaneous acquisition of two images—with/without strong artificial lighting (“Flash”/“no-Flash”). The difference between these images represents the appearance of the target scene as if only the artificial light was present, allowing a tight control over ambient light for color-based detection. A performance evaluation database was acquired in greenhouse conditions using an eye-in-hand RGB camera mounted on a robotic manipulator. The database includes 156 scenes with 468 images containing a total of 344 yellow sweet peppers. Performance of both color blob and deep-learning detection algorithms are compared on Flash-only and FNF images. The collected database is made public.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 19, no 6, article id 1390
Keywords [en]
Flash-No-Flash, outdoor vision, fruit detection, autonomous harvesting
National Category
Signal Processing
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-79407DOI: 10.3390/s19061390ISI: 000465520200079PubMedID: 30901837Scopus ID: 2-s2.0-85063686542OAI: oai:DiVA.org:oru-79407DiVA, id: diva2:1388952
Note

This research was partially funded by the European Commission grant number 644313 and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological and Cognitive Robotics Initiative, the Marcus Endowment Fund, and the Rabbi W. Gunther Plaut Chair in Manufacturing Engineering.

Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-01-28Bibliographically approved

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Kurtser, Polina

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