A Novel Fire Identification Algorithm Based on Improved Color Segmentation and Enhanced Feature Data
| Autor: | Qing An, Kegen Yu, Xijiang Chen, Ya Ban |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Feature data
Fire detection business.industry Computer science Deep learning 020208 electrical & electronic engineering Centroid YCbCr 02 engineering and technology Image segmentation 0202 electrical engineering electronic engineering information engineering Segmentation Artificial intelligence Electrical and Electronic Engineering business Hidden Markov model Instrumentation Algorithm |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement. 70:1-15 |
| ISSN: | 1557-9662 0018-9456 |
| DOI: | 10.1109/tim.2021.3075380 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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