- Tytuł:
- Estimating the distance to an object from grayscale stereo images using deep learning
- Autorzy:
- Kulawik, Joanna
- Powiązania:
- https://bibliotekanauki.pl/articles/2202043.pdf
- Data publikacji:
- 2022
- Wydawca:
- Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
- Tematy:
-
estimating distance
stereo vision
convolutional neural network
deep learning
szacowanie odległości
widzenie stereoskopowe
konwolucyjne sieci neuronowe
uczenie głębokie - Opis:
- 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.
- Źródło:
-
Journal of Applied Mathematics and Computational Mechanics; 2022, 21, 4; 60--72
2299-9965 - Pojawia się w:
- Journal of Applied Mathematics and Computational Mechanics
- Dostawca treści:
- Biblioteka Nauki
Artykuł