Guidance for Visually Impaired Person through Braille Block Detection by Deep Learning

Abstract

In this paper, we propose a passage guidance method for the visually impaired person through a braille block detection. The proposed method recognizes the passage information by detecting the braille blocks individually through a neural network based on a captured image. The braille blocks are detected through YOLOv7 which is a state-of-the-art object detection network. Then, the placements of the detected braille blocks are analyzed to find the groups of the straight blocks arranged in a single line and of the dot blocks gathered in a square shape. The passage information is recognized by comparing between the analyzed block placement and predefined placements. Objects in a sidewalk are detected together with the braille block to warn obstacles on the sidewalk. The passage information is guided in voice for the visually impaired person. In simulation results, the proposed method recognized the passage information with about 85% accuracy.

Braille block detection at shortest distance by mobile devices

“…Estimation of the layout of Braille blocks has been investigated in various situations [1][2] [3]. However, when we think of the intelligent support of walking assistance for the visually impaired, we have to cope with various kinds of bad-looking Braille blocks due to dust and/or time-related deterioration.…”