Fire Detection in Images Using Framework Based on Image Processing, Motion Detection and Convolutional Neural Network




Fire detection, Flame detection, Image processing, Motion Detection, Transfer learning


Fire detection in images has been frequently used recently to detect fire at an early stage. These methods play an important role in reducing the loss of life and property. Fire is not only chemically complex, but also physically very complex. The shape and color of the flame varies according to the type of fuel in the fire. This has made fire detection a very challenging problem. Advanced image processing algorithms are also needed to accurately detect fire. To solve this problem, a three-stage fire framework was created in this study. In the first stage, the flame region was extracted from the images containing the fire region with the basic image processing algorithms. At this stage, reduce brightness, HSL, YCbCr, median and herbaceous filters are applied successively to the image. Since the flame image has a polygonal structure by nature, the number of edges of the flame region has been found. In the second stage, the mobility feature of the flame was utilized. For this purpose, the mobility of the flame was determined by comparing the video frames containing the fire image. In the third stage, the CNN method was used to detect the fire in the images. The CNN model was trained with the transfer learning method using the Inception V3, SequeezeNet, VGG16 and VGG19 trained models. As a result of the tests of the models, 98.8%, 97.0%, 97.3% and 96.8% classification success were obtained, respectively. With the proposed fire detection framework, it is thought that the damage caused by the fire can be reduced by early detection of the fire and timely intervention.


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Author Biography

Murat Koklu, Selcuk University

Depertman of computer engineering




How to Cite

Y. S. Taspinar, M. Koklu, and M. Altin, “Fire Detection in Images Using Framework Based on Image Processing, Motion Detection and Convolutional Neural Network”, Int J Intell Syst Appl Eng, vol. 9, no. 4, pp. 171–177, Nov. 2021.



Research Article

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