Estimating the distance to an object from grayscale stereo images using deep learning

• Autorzy: Kulawik Joanna

Identyfikatory

DOI

10.17512/jamcm.2022.4.06

• Języki publikacji: EN

Abstrakty

EN

This article presents an innovative proposal for estimating the distance between an autonomous vehicle and an object in front of it. Such information can be used, for example, to support the process of controlling an autonomous vehicle. The primary source of information in research is monochrome stereo images. The images were made in compliance with the laws of the canonical order. The developed convolutional neural network model was used for the estimation. A proprietary dataset was developed for the experiments. The analysis was based on the phenomenon of disparity in stereo images. As a result of the research, a correctly trained model of the CNN network was obtained in six variants. High accuracy of distance estimation was achieved. This publication describes an original proposal for a hybrid blend of digital image analysis, stereo-vision, and deep learning for engineering applications.

Słowa kluczowe

EN

estimating distance; stereo vision; convolutional neural network; deep learning

PL

szacowanie odległości; widzenie stereoskopowe; konwolucyjne sieci neuronowe; uczenie głębokie

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2022
• Tom: Vol. 21, nr 4
• Strony: 60--72
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Kulawik Joanna

• Department of Computer Science, Czestochowa University of Technology Czestochowa, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).