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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. 13.2% 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%
2.2k
per year above
Top-10% rate
13.2%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+102%
the tick is no change

Which countries lead Fire Detection and Safety Systems research?

By volume, China and India publish the most (2.5k and 1.3k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.5k works
  2. 2 India 1.3k works
  3. 3 United States 434 works
  4. 4 Indonesia 216 works
  5. 5 South Korea 188 works
  6. 6 ?? 137 works
  7. 7 Türkiye 109 works
  8. 8 Japan 95 works
  9. 9 Malaysia 92 works
  10. 10 Canada 81 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 48.6%India: 25.1%United States: 8.5%Indonesia: 4.2%6 others listed: 13.7%49%largest
China2,492 · 48.6%India1,287 · 25.1%United States434 · 8.5%Indonesia216 · 4.2%6 others listed702 · 13.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Fire Detection and Safety Systems research?

By volume in 2022–2025, Saveetha University publishes the most Fire Detection and Safety Systems research, followed by China University of Mining and Technology and SRM Institute of Science and Technology.

Who are the leading researchers in Fire Detection and Safety Systems?

The most-cited researchers publishing on Fire Detection and Safety Systems include Lei Zhang and Ling Shao.

  1. 1 Lei Zhang Hong Kong 5.3k citations
  2. 2 Ling Shao United Arab Emirates 4.1k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Fire Detection and Safety Systems research done?

The largest centres of Fire Detection and Safety Systems research in 2022–2025 are Beijing (China), Chennai (India), Xi'an (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Vijayawada.

Largest cities, 2022–2025

  1. 1 Beijing China 404 works
  2. 2 Chennai India 206 works
  3. 3 Xi'an China 141 works
  4. 4 Nanjing China 140 works
  5. 5 Shanghai China 126 works
  6. 6 Wuhan China 120 works
  7. 7 Chengdu China 98 works
  8. 8 Coimbatore India 90 works
  9. 9 Tianjin China 80 works
  10. 10 Hefei China 80 works

Where it is the local speciality

  1. VijayawadaIN · 33.0 works11×
← less than its size predictsmore →

Location quotient: how much more of its research is in Fire Detection and Safety Systems than the world average.

See Fire Detection and Safety Systems on the map

Where is the best place to study Fire Detection and Safety Systems?

Among universities, judged by research, Saveetha University, Gachon University and Sejong University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 30.33%fractional works in this node (log) →share in the world top 10% →Saveetha University: 54, 17.9%Gachon University: 12, 46.6%Sejong University: 8, 52.2%Hong Kong Polytechnic University: 21, 56.0%Amrita Vishwa Vidyapeetham: 33, 17.4%Institute of Engineering: 20, 5.6%Vellore Institute of Technology University: 40, 12.1%Nanjing Forestry University: 15, 50.4%China University of Mining and Technology: 47, 20.8%Xi'an University of Science and Technology: 26, 24.3%Hong Kong Polytechni…Sejong UniversityGachon UniversitySaveetha University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Saveetha UniversityIndia 60.517.9%8.8×54
2 Gachon UniversitySouth Korea 60.446.6%8.8×12 +47.4%
3 Sejong UniversitySouth Korea 58.252.2%7.3×8
4 Hong Kong Polytechnic UniversityHong Kong 57.956.0%3.7×21 -60.9%
5 Amrita Vishwa VidyapeethamIndia 56.617.4%9.6×33 +71.4%
6 Institute of EngineeringNepal 56.05.6%15.9×20 +145.3%
7 Vellore Institute of Technology UniversityIndia 54.912.1%6.3×40 +1855.6%
8 Nanjing Forestry UniversityChina 54.250.4%5.3×15
9 China University of Mining and TechnologyChina 53.820.8%9.2×47 -62.0%
10 Xi'an University of Science and TechnologyChina 52.224.3%17.4×26 -27.8%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Fire Detection and Safety Systems research growing?

Output in 2018–2022 was 102% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Fire Detection and Safety Systems.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.