GAN and semi-supervised learning-based indoor visible light localization system for image sensors

Duiqiang Chen; Lizhen Cui; Ling Qin; Fengying Wang

doi:10.1117/12.3029632

21 June 2024 GAN and semi-supervised learning-based indoor visible light localization system for image sensors

Duiqiang Chen, Lizhen Cui, Ling Qin, Fengying Wang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670P (2024) https://doi.org/10.1117/12.3029632
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Aiming at the visible light localization system in the fingerprint acquisition link, which is time-consuming and inefficient, this paper proposes a visible light localization system based on generative adversarial network and semi-supervised learning to realize high-efficiency fingerprint acquisition and high-precision localization. Firstly, the image of the lamp is captured using a CMOS smartphone, and the fingerprint database is constructed by feature extraction of the feature image of the lamp using image processing technology, which can increase the diversity and richness of the fingerprint database; secondly, the unlabeled pseudo-fingerprint data is generated by GAN network, which expands the dataset, thus reduces the cumbersomeness of fingerprint acquisition and improves the efficiency of fingerprint acquisition; lastly, the real fingerprint data and pseudo-fingerprint data are mixed with semi-supervised training of CNN models for location prediction. The experimental results show that in the experimental space of 2.25m×1.65m×3m, only 1564 labeled fingerprint data are collected in this paper, and the achieved average error of localization is 2.48cm. among them, the probability of error less than 5cm accounts for 90%, which meets the indoor localization requirements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Duiqiang Chen, Lizhen Cui, Ling Qin, and Fengying Wang "GAN and semi-supervised learning-based indoor visible light localization system for image sensors", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670P (21 June 2024); https://doi.org/10.1117/12.3029632

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KEYWORDS

Data modeling

Education and training

Visible radiation

Gallium nitride

Machine learning

Light emitting diodes

Image processing

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