Controlled lighting and illumination-independent target detection for real-time cost-efficient applications. The case study of sweet pepper robotic harvesting

Boaz Arad, Polina Kurtser, Ehud Barnea, Ben Harel, Yael Edan, Ohad Ben-Shahar

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

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 colorand 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.

Original languageEnglish
Article number1390
JournalSensors
Volume19
Issue number6
DOIs
StatePublished - 2 Mar 2019

Keywords

  • Autonomous harvesting
  • Flash-no-flash
  • Fruit detection
  • Outdoor vision

Fingerprint

Dive into the research topics of 'Controlled lighting and illumination-independent target detection for real-time cost-efficient applications. The case study of sweet pepper robotic harvesting'. Together they form a unique fingerprint.

Cite this