Fire Detection and Safety Systems
Fire Detection and Safety Systems is a research topic within Safety, Risk, Reliability and Quality. Science Explorer counts 17k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.
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.
- Computer Vision
- Fire Detection
- Smoke Detection
- Convolutional Neural Networks
- Video Surveillance
- Forest Fire Monitoring
- Unmanned Aerial Vehicles
- Image Processing
- Real-Time Detection
- IoT-Based Systems
- Research works
- 17k fractional, since 1950
- In the world top 10%
- 1.7k per year above
- Top-10% rate
- 9.9% share of its works in the world top 10%
- Growth, 2018-21 → 2022-25
- +117% the tick is no change
- Largest producer
China
48% of the 10 largest, 2022-now
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