Braille block detection at shortest distance by mobile devices

Shotaro Nakamura; Hidehiko Shishido; Yoshinari Kameda

doi:10.1117/12.2666662

25 March 2023 Braille block detection at shortest distance by mobile devices

Shotaro Nakamura, Hidehiko Shishido, Yoshinari Kameda

Author Affiliations +

Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 125921R (2023) https://doi.org/10.1117/12.2666662
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of

Abstract

This research proposes a method based on image recognition technology to guide the visually impaired to the shortest Braille block in situations where they cannot find the existence of Braille blocks. In this paper, we make four proposals. First, we propose a method for detecting Braille blocks using SSD, a deep learning network with our own image dataset. Various kinds of Braille blocks and out-of-specification Braille blocks look different by weathering indoors and outdoors. Second, we propose a detection method that can handle differences in camera height. By constructing a training dataset with images of Braille blocks taken at a person's height or a general robot, we can achieve detection with cameras of different heights. Third, we propose a standalone method, real-time recognition of Braille blocks using the cameras of mobile devices. We incorporate MobileNet, a lightweight deep-learning network, into the SSD network. As a result, we achieve standalone and real-time Braille block recognition by optimizing the network for mobile devices. Finally, we propose a method of estimating the orientation and shortest distance of Braille blocks. It considers the continuous and linear arrangement pattern of guiding blocks that indicate the direction of travel.

Citation Download Citation

Shotaro Nakamura, Hidehiko Shishido, and Yoshinari Kameda "Braille block detection at shortest distance by mobile devices", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 125921R (25 March 2023); https://doi.org/10.1117/12.2666662

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