2000 character limit reached
Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks (2401.11582v1)
Published 21 Jan 2024 in cs.CV, cs.LG, and eess.IV
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.
- “A review of machine learning applications in wildfire science and management,” Environmental Reviews, vol. 28, no. 4, pp. 478–505, 2020.
- “Wildfire-grazing impact on forage quality assessed with near-infrared spectroscopy and generalized partial least squares regression,” Rangeland Ecology & Management, vol. 87, pp. 132–140, 2023.
- “Wildfire monitoring in remote areas using autonomous unmanned aerial vehicles,” in IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2019, pp. 835–840.
- “Wildland fire detection and monitoring using a drone-collected rgb/ir image dataset,” IEEE Access, vol. 10, pp. 121301–121317, 2022.
- A. Shamsoshoara and F. Afghah, Airborne Fire Detection and Modeling Using Unmanned Aerial Vehicles Imagery: Datasets and Approaches, pp. 525–550, Springer International Publishing, Cham, 2023.
- “Wildfire spread modeling with aerial image processing,” in 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), 2020, pp. 335–340.
- “Ic-gan: An improved conditional generative adversarial network for rgb-to-ir image translation with applications to forest fire monitoring,” Expert Systems with Applications, vol. 238, pp. 121962, 2024.
- “Stability analysis and controller synthesis using single-hidden-layer relu neural networks,” IEEE Transactions on Automatic Control, 2023.
- H. Behjoo and M. Chertkov, “Exact fractional inference via re-parametrization & interpolation between tree-re-weighted-and belief propagation-algorithms,” arXiv preprint arXiv:2301.10369, 2023.
- “Automated stability analysis of piecewise affine dynamics using vertices,” arXiv preprint arXiv:2307.03868, 2023.
- “Integrated optimization of powertrain energy management and vehicle motion control for autonomous hybrid electric vehicles,” IEEE Transactions on Vehicular Technology, 2023.
- “Attentiongan: Unpaired image-to-image translation using attention-guided generative adversarial networks,” IEEE transactions on neural networks and learning systems, 2021.
- “A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms,” Signal Processing, vol. 190, pp. 108309, 2022.
- “Forest fire and smoke detection using deep learning-based learning without forgetting,” Fire ecology, vol. 19, no. 1, pp. 1–17, 2023.
- “A deep learning approach to downscale geostationary satellite imagery for decision support in high impact wildfires,” Forests, vol. 12, no. 3, pp. 294, 2021.
- “Drone-based rgb-infrared cross-modality vehicle detection via uncertainty-aware learning,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 10, pp. 6700–6713, 2022.
- “Flame 2: Fire detection and modeling: Aerial multi-spectral image dataset,” 2022.
- “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020.
- “Fire-gan: A novel deep learning-based infrared-visible fusion method for wildfire imagery,” Neural Computing and Applications, pp. 1–13, 2021.
- “Attention-guided synthetic data augmentation for drone-based wildfire detection,” in IEEE INFOCOM 2023-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2023, pp. 1–6.
- “A generative adversarial network (gan) approach to creating synthetic flame images from experimental data,” Energy and AI, vol. 13, pp. 100238, 2023.
- “Dualgan: Unsupervised dual learning for image-to-image translation,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2849–2857.
- “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8789–8797.
- “An unpaired thermal infrared image translation method using gma-cyclegan,” Remote Sensing, vol. 15, no. 3, pp. 663, 2023.
- “Gan-based sar and optical image translation for wildfire impact assessment using multi-source remote sensing data,” Remote Sensing of Environment, vol. 289, pp. 113522, 2023.
- A. Borji, “Pros and cons of GAN evaluation measures,” Computer vision and image understanding, vol. 179, pp. 41–65, 2019.