Traditional Sensor-Based and Computer Vision-Based Fire Detection Systems: A Review
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Fire detection
Computer vision
Convolutional Neural Network
Deep learning

How to Cite

Hassan, A., & Audu, A. I. (2022). Traditional Sensor-Based and Computer Vision-Based Fire Detection Systems: A Review. ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT, 18(3), 469-492. Retrieved from


Fire emergency is one of the dominant disasters which is threatening human society, infrastructure, and the environment in the world. Fire disaster can lead to a great number of human casualties, loss of vast natural resources and serious infrastructural damage. To minimize these such catastrophic menace, protect and save human lives and infrastructure, substantial research efforts have been devoted to developing fire detection systems using different approaches. This paper discusses fire detection systems and summarizes the state-of–the-art achievements. A total number of 60 publications related to fire detection methods are extracted from IEEE and Google Scholar database and analysed. Different fire detection methods are reviewed, and their merits and shortcomings are highlighted. The literature review presented in this paper illustrates the advantages of computer vision-based fire detection approaches over the traditional sensor-based approaches which are characterised by limited detection range, transport delay, and high false alarm. In addition, it shows that the high hardware requirement and computational complexity remain the key drawbacks of the computer vision-based methods and most of the research works made no attempt to implement such methods on embedded hardware platform such as FPGA. Further research efforts are required to design, develop, and implement computer vision-based systems for fire detection on embedded hardware targets in an efficient and computationally less intensive manner.


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