Fire Segmentation

Fire segmentation, the task of automatically identifying fire pixels in images and videos, is crucial for rapid wildfire detection and response. Current research emphasizes improving the speed and accuracy of fire segmentation models, often employing convolutional neural networks (CNNs) enhanced with techniques like attention mechanisms and hardware acceleration for real-time processing on drones. A key challenge involves overcoming limitations in training data, with efforts focusing on weakly-supervised learning and the creation of new, publicly available datasets that specifically address the complexities of thermal imaging in wildfire scenarios. These advancements are vital for improving autonomous fire detection systems and enhancing wildfire management strategies.