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A Novel Fire Identification Algorithm Based on Improved Color Segmentation and Enhanced Feature Data
IEEE Transactions on Instrumentation and Measurement ( IF 5.6 ) Pub Date : 2021-04-26 , DOI: 10.1109/tim.2021.3075380
Xijiang Chen , Qing An , Kegen Yu , Ya Ban

In order to improve the accuracy of fire identification based on video in the Internet-of-Things environment, this article proposes a new fire identification algorithm by merging fire segmentation and multifeature fusion of fire. First, according to the relationship between R and Y channels, the improved YCbCr models are established for initial fire segmentation under reflection and nonreflection conditions, respectively. Simultaneously, the reflection and nonreflection conditions are judged by comparing the areas obtained by the two improved YCbCr models. Second, an improved region growing algorithm is proposed for fine fire segmentation by making use of the relationship between the seed point and its adjacent points. The seed points are determined using the weighted average of centroid coordinates of each segmented image. Finally, the quantitative indicators of fire identification are given according to the variation coefficient of fire area, the dispersion of centroid, and the circularity. Extensive experiments were conducted, and the experimental results demonstrate that the proposed fire detection method considerably outperforms the traditional methods on average in terms of three performance indexes: precision, recall, and $F1$ -score. Specifically, compared with the deep learning method, the precision of the proposed method is slightly higher. Although the recall of the proposed method is slightly lower than the deep learning method, its computation complexity is low.

中文翻译:

基于改进颜色分割和增强特征数据的火灾识别新算法

为了提高物联网环境中基于视频的火灾识别的准确性,本文提出了一种将火灾分割与火灾多特征融合相结合的新的火灾识别算法。首先,根据R和Y通道之间的关系,建立了改进的YCbCr模型,分别用于反射和非反射条件下的初始火分割。同时,通过比较两个改进的YCbCr模型获得的面积来判断反射和非反射条件。其次,利用种子点及其邻近点之间的关系,提出了一种改进的区域增长算法,用于细火分割。使用每个分割图像的质心坐标的加权平均值确定种子点。最后,根据着火面积的变化系数,质心的离散度和圆度,给出了火灾识别的定量指标。进行了广泛的实验,实验结果表明,所提出的火灾探测方法在三个性能指标上的平均性能大大优于传统方法:精确度,召回率和 $ F1 $ -分数。具体而言,与深度学习方法相比,该方法的精度稍高。尽管该方法的召回率比深度学习方法略低,但其计算复杂度却很低。
更新日期:2021-05-07
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