基于改进MSER算法的电力设备红外故障区域提取方法

Fault region extraction using improved MSER algorithm with application to the electrical system

红外图像处理中因目标边界模糊、区域灰度变化等因素,导致传统的极大稳态区域方法区域提取效果低下。为此,提出一种基于改进极大稳态区域方法的电力设备红外故障区域提取机制,提升区域提取效果。首先,从灰度相似度聚类出发,采用Meanshift算法对分割区域的邻域像素进行聚类。其次,结合阈值分割机制,快速将相似像素进行分割,最终通过迭代得到电力设备故障所呈现的亮度区域信息。实验结果表明该提取区域方法性能优于极大稳态区域算法,具有较低的误分类错误,且相比于Mean shift算法,具有高效的处理速度。

Aiming at the problem of blur boundary and the intensity variation of regions in the infrared image,the traditional Maximally Stable Extremal Region(MSER)may fail to detect the region,thus leading to the poor performance.Therefore,the improved MSER algorithm is proposed in this paper to find the fault region in infrared electrical equipment image,which is based on the intensity similarity clustering.At first,the mean shift algorithm is used to cluster the pixels with similarity from the point of viewing of intensity homogeneity.Second,the thresholding mechanism is utilized to get the fast binary image,where the thresholding is selected from the low intensity of pixels clustered into the region.It thereby can split the pixels with similarity together faster,and the bright region corresponding to the fault region can be obtained through the iteration.Finally,experiments on the electrical equipment with infrared image show that the proposed method has better performance than the original MSER and owns lower misclassification error.Meanwhile,it decreases the time consumption as comparing to the Mean shift algorithm.