Path Segmentation with Artificial Neural Networks in Low Structured Environments for the Navigation of Visually Impaired People

Seßner J, Schmid M, Lauer-Schmalz M, Franke J (2020)

Publication Type: Conference contribution

Publication year: 2020

Publisher: IEEE Computer Society

Book Volume: 2020-November

Pages Range: 1242-1247

Conference Proceedings Title: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

Event location: New York City, NY US

ISBN: 9781728159072

DOI: 10.1109/BioRob49111.2020.9224324

Abstract

The number of visually impaired people is constantly increasing. Mobility aids such as white canes are designed to help these people navigate in their everyday life. However, these systems have limitations in certain environments or activities. To meet this challenge, we propose an assistance system to support the navigation of visually impaired people in low structured environments. The system captures information from its environment with a stereo camera. The disparity and color images are processed by an embedded computer which calculates a safe walking direction and accordingly provides vibrotactile feedback to the visually impaired person. This paper presents an approach to segment the traversable path in the captured color image of low structured environments using artificial neural networks. Therefore, a dataset is generated and several encoder-decoder architectures are evaluated and optimized to achieve a sufficient accuracy and real-time framerate of the binary segmentation on a mobile computer. The optimized architecture manages to segment the path with a test Intersection over Union value of 0.9378 and a segmentation time of 96 ms per image in various low structured environments.

Authors with CRIS profile

Julian Seßner Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

How to cite

APA:

Seßner, J., Schmid, M., Lauer-Schmalz, M., & Franke, J. (2020). Path Segmentation with Artificial Neural Networks in Low Structured Environments for the Navigation of Visually Impaired People. In Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (pp. 1242-1247). New York City, NY, US: IEEE Computer Society.

MLA:

Seßner, Julian, et al. "Path Segmentation with Artificial Neural Networks in Low Structured Environments for the Navigation of Visually Impaired People." Proceedings of the 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020, New York City, NY IEEE Computer Society, 2020. 1242-1247.

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