FIRE DETECTION WITH SURVILENCE CAMERA USING DEEP LEARNING

In order to improve the accuracy of fifire identifification based on video in the Internet-of-Things environment, this article proposes a new fifire identifification algorithm by merging fifire segmentation and multifeature fusion of fifire. First, according to the relationship between R and Y channels, the improved YCbCr models are established for initial fifire segmentation under reflection and nonreflflection conditions, respectively. Second, an improved region growing algorithm is proposed for fifine fifire segmentation by making use of the relationship between the seed point and its adjacent points. Finally, the quantitative indicators of fifire identifification are given according to the variation coeffificient of fifire area, the dispersion of centroid, and the circularity. Extensive experiments were conducted, and the experimental results demonstrate that the proposed fifire detection method considerably outperforms the traditional methods on average in terms of three performance indexes: precision, recall, and F1-score.