DeepScanner: A Robotic System for Automated 2D Object Dataset Collection with Annotations

Valery Ilin, Ivan Kalinov, Pavel Karpyshev, Dzmitry Tsetserukou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the proposed study, we describe the possibility of automated dataset collection using an articulated robot. The proposed technology reduces the number of pixel errors on a polygonal dataset and the time spent on manual labeling of 2D objects. The paper describes a novel automatic dataset collection and annotation system, and compares the results of automated and manual dataset labeling. Our approach increases the speed of data labeling 240-fold, and improves the accuracy compared to manual labeling 13-fold. We also present a comparison of metrics for training a neural network on a manually annotated and an automatically collected dataset.

Original languageEnglish
Title of host publicationProceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728129891
DOIs
Publication statusPublished - 2021
Event26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 - Virtual, Vasteras, Sweden
Duration: 7 Sep 202110 Sep 2021

Publication series

NameIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2021-September
ISSN (Print)1946-0740
ISSN (Electronic)1946-0759

Conference

Conference26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Country/TerritorySweden
CityVirtual, Vasteras
Period7/09/2110/09/21

Keywords

  • Automated data annotation
  • Dataset collection
  • Image segmentation
  • Industrial robot

Fingerprint

Dive into the research topics of 'DeepScanner: A Robotic System for Automated 2D Object Dataset Collection with Annotations'. Together they form a unique fingerprint.

Cite this