Fire identification based on improved multi feature fusion of YCbCr and regional growth

This is the authors' abstract. We don't add key points for this paper.

• The improved YCbCr models are established for initial fire segmentation under reflection and non-reflection conditions. • We propose entropy function of the average contrast and uniformity to determine the optimal threshold of region growth. • Judgment index is proposed by the variation coefficient of area, dispersion of centroid and circularity . Fire is one of the mutable hazards that damage properties and destroy forests. However, fire-like objects easily influence many existing image-based fire detection methods. In order to improve the performance of fire identification, this paper proposes a new fire identification algorithm by merging fire segmentation and multi feature fusion of fire. First, the improved YCbCr models in the reflection and non-reflection environment are constructed according to the color model. Simultaneously, the reflection and non-reflection conditions can be judged according to the segmented area. Second, the seed points are determined according to the weighted average of centroid of each connected region. Simultaneously, the fine segmentation of fire image is implemented according to the entropy of average contrast and uniformity within the connected region. Experiments show that the segmentation is not affected by image noises. Finally, the quantitative indicators of fire identification are given according to the coefficient of variation of area, the dispersion of centroid and the circularity. Cases show that the proposed identification method of fire not only accurately identifies fire, but also has a lower computation complexity than the deep learning method.