Fire Detection and Safety Systems
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.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.