摘要
提出一种基于熵值加权支持向量机(SVM)的火焰检测方法。采用三帧差分算法对视频前景提取(VIBE)算法进行改进,并提出TH-VIBE前景检测算法,提升疑似火焰区域获取的准确率与完整性;利用熵值加权降低纹理特征、面积变化特征、圆形度特征、灰度特征等特征数据的冗余程度并建立熵值加权火焰识别模型,提升火焰识别速率与准确率;最后依据韩国启明大学和土耳其比尔肯大学SPG工作组火焰数据进行实验,火焰检测准确率可达97%,具有较高的鲁棒性。
A flame detection method based on entropy weighted Support Vector Machine(SVM)is proposed.Firstly,the three-frame difference algorithm is utilized to improve the Visual Background Extractor(VIBE)algorithm,and the Three VIBE(TH-VIBE)foreground detection algorithm is proposed to improve the accuracy and integrity of the acquisition of the suspected flame area.Secondly,entropy weighting is adopted to reduce the redundancy degree of feature data such as texture feature,area change feature,roundness feature and gray level feature,and an entropy-weighted flame recognition model is established to improve the flame recognition rate and accuracy.Finally,based on the flame data from Keimyung University in South Korea and SPG working group of Bilkent University in Turkey,the flame detection accuracy can reach 97%with high robustness.
作者
王彦朋
柴文
王晓君
WANG Yanpeng;CHAI Wen;WANG Xiaojun(School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang Hebei 050000,China)
出处
《太赫兹科学与电子信息学报》
2021年第3期458-464,共7页
Journal of Terahertz Science and Electronic Information Technology
基金
河北科技大学五大平台开发基金课题资助项目(2018PT13)。
