Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks

• URL: http://arxiv.org/abs/2401.11582v1
• Date: Sun, 21 Jan 2024 20:10:02 GMT
• Title: Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks
• Authors: Hossein Rajoli, Pouya Afshin, Fatemeh Afghah
• Abstract summary: Unmanned aerial vehicles (UAVs) offer a flexible and cost-effective solution for wildfire monitoring. 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.
• Score: 5.343932820859596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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