oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Dynamic thresholding algorithm for robotic apple detection
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel .ORCID iD: 0000-0001-6146-1423
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel .ORCID iD: 0000-0003-4685-379x
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel .ORCID iD: 0000-0002-7430-8468
2017 (English)In: 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), IEEE, 2017, p. 240-246Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a dynamic thresholding algorithm for robotic apple detection. The algorithm enables robust detection in highly variable lighting conditions. The image is dynamically split into variable sized regions, where each region has approximately homogeneous lighting conditions. Nine thresholds were selected so as to accommodate three different illumination levels for three different dimensions in the natural difference index (NDI) space by quantifying the required relation between true positive rate and false positive rate. This rate can change along the robotic harvesting process, aiming to decrease FPR from far views (to minimize cycle times) and to increase TPR from close views (to increase grasping accuracy). Analyses were conducted on apple images acquired in outdoor conditions. The algorithm improved previously reported results and achieved 91.14% true positive rate (TPR) with 3.05% false positive rate (FPR) using the NDI first dimension and a noise removal process.

Place, publisher, year, edition, pages
IEEE, 2017. p. 240-246
Keywords [en]
Apples detection, Dynamic thresholding, Object detection, Robotic harvesting
National Category
Signal Processing
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-79438DOI: 10.1109/ICARSC.2017.7964082Scopus ID: 2-s2.0-85026853572ISBN: 978-1-5090-6235-5 (print)ISBN: 978-1-5090-6234-8 (electronic)OAI: oai:DiVA.org:oru-79438DiVA, id: diva2:1389029
Conference
IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2017), Coimbra, Portugal- April 26-28, 2017
Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-03-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Kurtser, Polina

Search in DiVA

By author/editor
Zemmour, ElieKurtser, PolinaEdan, Yael
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 21 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf