This cluster of papers focuses on the development and application of computer vision, deep learning, and image processing techniques for real-time fire and smoke detection, particularly in the context of video surveillance, forest fire monitoring, and unmanned aerial vehicle (UAV) based systems. The research covers a wide range of methods including convolutional neural networks, statistical color models, multi-feature fusion, and IoT-based intelligent modeling for fire prevention and safety.