Semantic Image Segmentation Methods in the Duckietown Project

Kristina S. Lanchukovskaya, Dasha E. Shabalina, Tatiana V. Liakh

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

Abstract

The article focuses on evaluation of the applicability of existing semantic segmentation algorithms for the Duckietown simulator. Duckietown is an open research project in the field of autonomously controlled robots. The article explores classical semantic image segmentation algorithms. Their analysis for applicability in Duckietown is carried out. With the help of them, we want to make a dataset for training neural networks. The following was investigated: edge-detection techniques, threshold algorithms, region growing, segmentation algorithms based on clustering, neural networks. The article also reviewed networks designed for semantic image segmentation and machine learning frameworks, taking into account all the limitations of the Duckietown simulator. Experiments were conducted to evaluate the accuracy of semantic segmentation algorithms on such classes of Duckietown objects as road and background. Based on the results of the analysis, region growing algorithms and clustering algorithms were selected and implemented. Experiments were conducted to evaluate the accuracy on such classes of Duckietown objects as road, background and traffic signs. After evaluating the accuracy of the algorithms considered, it was decided to use Color segmentation, Mean Shift, Thresholding algorithms and Segmentation of signs by April-tag for image preprocessing. For neural networks, experiments were conducted to evaluate the accuracy of semantic segmentation algorithms on such classes of Duckietown objects as road and background. After evaluating the accuracy of the algorithms considered, it was decided to select the DeepLab-v3 neural network. Separate module was created for semantic image segmentation in Duckietown.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE 23rd International Conference of Young Professionals in Electron Devices and Materials, EDM 2022
PublisherIEEE Computer Society
Pages611-617
Number of pages7
ISBN (Electronic)9781665498043
DOIs
Publication statusPublished - 2022
Event23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022 - Altai, Russian Federation
Duration: 30 Jun 20224 Jul 2022

Publication series

NameInternational Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
Volume2022-June
ISSN (Print)2325-4173
ISSN (Electronic)2325-419X

Conference

Conference23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022
Country/TerritoryRussian Federation
CityAltai
Period30.06.202204.07.2022

Keywords

  • artificial intelligence
  • computer vision
  • duckiebots
  • Duckietown
  • neural networks
  • OpenCV
  • robotics
  • semantic image segmentation

OECD FOS+WOS

  • 1.02 COMPUTER AND INFORMATION SCIENCES

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