Advanced Deep Learning Techniques for High-Quality Synthetic Thermal Image Generation

Vicente Pavez, Gabriel Hermosilla (), Manuel Silva and Gonzalo Farias
Additional contact information
Vicente Pavez: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
Gabriel Hermosilla: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
Manuel Silva: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
Gonzalo Farias: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile

Mathematics, 2023, vol. 11, issue 21, 1-16

Abstract: In this paper, we introduce a cutting-edge system that leverages state-of-the-art deep learning methodologies to generate high-quality synthetic thermal face images. Our unique approach integrates a thermally fine-tuned Stable Diffusion Model with a Vision Transformer (ViT) classifier, augmented by a Prompt Designer and Prompt Database for precise image generation control. Through rigorous testing across various scenarios, the system demonstrates its capability in producing accurate and superior-quality thermal images. A key contribution of our work is the development of a synthetic thermal face image database, offering practical utility for training thermal detection models. The efficacy of our synthetic images was validated using a facial detection model, achieving results comparable to real thermal face images. Specifically, a detector fine-tuned with real thermal images achieved a 97% accuracy rate when tested with our synthetic images, while a detector trained exclusively on our synthetic data achieved an accuracy of 98%. This research marks a significant advancement in thermal image synthesis, paving the way for its broader application in diverse real-world scenarios.

Keywords: deep learning; thermal imaging; face detection; generative models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/21/4446/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/21/4446/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:21:p:4446-:d:1268366

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().