Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911762140364800 |
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| author | Rajoli, Hossein Afshin, Pouya Afghah, Fatemeh |
| author_facet | Rajoli, Hossein Afshin, Pouya Afghah, Fatemeh |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_11582 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks Rajoli, Hossein Afshin, Pouya Afghah, Fatemeh Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2401.11582 |