Can I read Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks on EtoBox?

Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks by Rajoli, Hossein; Afshin, Pouya; Afghah, Fatemeh is a scholarly article available to read on EtoBox.

What is Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks about?

Unmanned aerial vehicles (UAVs) offer a flexible and cost-effective solution for wildfire monitoring. However, their widespread deployment during wildfires has been hindered by a lack of operational guidelines and concerns about potential interference with aircraft systems. Consequently, the progress in developing deep-learning models for wildfire detection and characterization using aerial images is constrained by the limited availability, size, and quality of existing datasets. This paper introduces a solution aimed at enhancing the quality of current aerial wildfire datasets to align with advancements in camera technology. The proposed approach offers a solution to create a comprehensive, standardized large-scale image dataset. This paper presents a pipeline based on CycleGAN to enhance wildfire datasets and a novel fusion method that integrates paired RGB images as attribute conditioning in the generators of both directions, improving the accuracy of the generated images.

Author

Rajoli, Hossein; Afshin, Pouya; Afghah, Fatemeh

Published

2024

Language

EN