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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.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 6, artikkel-id 1390Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
MDPI, 2019. Vol. 19, nr 6, artikkel-id 1390
Emneord [en]
Flash-No-Flash, outdoor vision, fruit detection, autonomous harvesting
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Forskningsprogram
Datavetenskap
Identifikatorer
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
Merknad

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.

Tilgjengelig fra: 2020-01-28 Laget: 2020-01-28 Sist oppdatert: 2020-01-28bibliografisk kontrollert

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