A Robust Comparative Study of Adaptative Reprojection Fusion Methods for Deep Learning Based Detection Tasks with RGB-Thermal Images

Enrique Heredia-Aguado, Marcos Alfaro-Pérez, María Flores, Luis Paya, David Valiente, Arturo Gil

2025

Abstract

Fusing visible and thermal imagery is a promising approach for robust object detection in challenging environments, taking advantage of the strengths from different spectral information. Building on previous work in static early fusion, we present a comparative study of adaptative reprojection fusion methods that exploit advanced projections and frequency-domain transforms to combine RGB and thermal data. We evaluate Principal Component Analysis, Factor Analysis, Wavelet and Curvelet-based fusion, all integrated into a YOLOv8 detection pipeline. Experiments are conducted on the LLVIP dataset, with a focus on methodological rigour and reproducibility. This research show promising results based on these methods comparing to previous early fusion methods. We discuss the implications for future research and the value of robust experimental design for advancing the state of the art in multispectral fusion.

Paper Citation

in Harvard Style

Heredia-Aguado E., Alfaro-Pérez M., Flores M., Paya L., Valiente D. and Gil A. (2025). A Robust Comparative Study of Adaptative Reprojection Fusion Methods for Deep Learning Based Detection Tasks with RGB-Thermal Images. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 313-320. DOI: 10.5220/0013761800003982

in Bibtex Style

@conference{icinco25,
author={Enrique Heredia-Aguado and Marcos Alfaro-Pérez and María Flores and Luis Paya and David Valiente and Arturo Gil},
title={A Robust Comparative Study of Adaptative Reprojection Fusion Methods for Deep Learning Based Detection Tasks with RGB-Thermal Images},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={313-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013761800003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - A Robust Comparative Study of Adaptative Reprojection Fusion Methods for Deep Learning Based Detection Tasks with RGB-Thermal Images
SN - 978-989-758-770-2
AU - Heredia-Aguado E.
AU - Alfaro-Pérez M.
AU - Flores M.
AU - Paya L.
AU - Valiente D.
AU - Gil A.
PY - 2025
SP - 313
EP - 320
DO - 10.5220/0013761800003982
PB - SciTePress