A training data augmentation approach using GAN for learning of tactile paving images

Hirofumi Hayasaka; Akio Kimura

doi:10.1117/12.2666962

25 March 2023 A training data augmentation approach using GAN for learning of tactile paving images

Hirofumi Hayasaka, Akio Kimura

Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 125920R (2023) https://doi.org/10.1117/12.2666962
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of

Abstract

This paper discusses an approach to training data augmentation for identifying and detecting tactile paving (braille) blocks in image with machine learning. Image recognition system that assists the visually impaired is necessary to efficiently identify the guiding blocks in the image, and machine learning, represented by CNNs, etc., is considered effective for this purpose. However, it is labor intensive to collect a sufficient number of training images from the pedestrian’s perspective in various environments, and furthermore, if the number of training data is insufficient, the final identification performance of the entire system will be degraded. To solve these problems, this paper attempts to generate new training data from a small number of tactile paving images by GAN(Generative Adversarial Network), and demonstrates through evaluation experiments that adding generated images to training data can stabilize learning and improve the rate of correct answers.

Citation Download Citation

Hirofumi Hayasaka and Akio Kimura "A training data augmentation approach using GAN for learning of tactile paving images", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 125920R (25 March 2023); https://doi.org/10.1117/12.2666962

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KEYWORDS

Education and training

Gallium nitride

Machine learning

Data modeling

Visualization

Adversarial training

Image quality

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