Abstract
Image fire detection technology can solve the problem of large space fire safety effectively. It is difficult to accurately divide the flame area because of the complex background of large space fire image, so it has a higher problem of false alarm. We propose a three-layer combination segmentation model, which use the differential technology, RGB color segmentation technology and morphological difference technology, the suspected area of the flame is obtained by excluding most of the interference. Some characteristics such as similarity measure, area change value, density, eccentricity ratio, offset distance of centroid point are extracted from suspected area of fire image. Finally, the fire identification model is established by RBF neural network, and the extracted flame characteristics is used as input to classify the fire images. A series of fire images and sample images have been experimented, the simulation results show that the algorithm can reduce the fire alarm rate effectively and improve the accuracy rate of fire alarm.
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Jin, L., Li, L. (2018). Research on Fire Image Detection Technology Base on RBF. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_59
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DOI: https://doi.org/10.1007/978-3-030-00018-9_59
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