Semantic Image Segmentation Methods in the Duckietown Project

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

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

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.

Язык оригиналаанглийский
Название основной публикацииProceedings of the 2022 IEEE 23rd International Conference of Young Professionals in Electron Devices and Materials, EDM 2022
ИздательIEEE Computer Society
Страницы611-617
Число страниц7
ISBN (электронное издание)9781665498043
DOI
СостояниеОпубликовано - 2022
Событие23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022 - Altai, Российская Федерация
Продолжительность: 30 июн. 20224 июл. 2022

Серия публикаций

НазваниеInternational Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
Том2022-June
ISSN (печатное издание)2325-4173
ISSN (электронное издание)2325-419X

Конференция

Конференция23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022
Страна/TерриторияРоссийская Федерация
ГородAltai
Период30.06.202204.07.2022

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  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ

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