Fire Detection

Fire detection research focuses on developing accurate and efficient systems for identifying fires across various environments, from forests and industrial settings to transportation vehicles. Current efforts leverage deep learning, particularly convolutional neural networks (CNNs) like YOLOv5 and SegNet, and diffusion models, often incorporating techniques like transfer learning and hardware acceleration to improve speed and performance in resource-constrained situations. These advancements are crucial for mitigating wildfire damage, enhancing industrial safety, and improving response times to fire incidents, ultimately reducing losses of life and property. The development of robust, real-time fire detection systems is a significant area of ongoing research, driven by the need for improved accuracy and efficiency in diverse and challenging conditions.