基于局部区域聚类的电力设备故障区域提取方法

针对电力设备红外图像诊断中热故障区域提取问题,提出了一种局部区域Mediodshift聚类的电力设备红外图像故障区域提取方法。文章根据热故障所表现的灰度特性初始化聚类中心;结合Mediodshift聚类方法,对目标区域邻域像素进行聚类。为了尽可能获取故障区域邻域相似像素,引入了基于邻域灰度的调节策略。同时,为了提高聚类效率,采用了自高向低的聚类阈值分割机制,从而使得Mediodshift算法能快速地将整幅图像中故障区域像素进行聚类,实现红外图像中热故障区域的提取。最后通过典型红外图像实验测试,验证了该方法区域提取的有效性,且对比目前现有的一些方法,进一步表明文中方法具有较好的故障区域提取性能。

Region extraction of electronic fault region using local clustering algorithm

Aiming at the problem of extracting thermal fault area in detecting power device using infrared imaging,a method of extracting thermal fault area is proposed,which is based on the Mediodshift with local clustering.Firstly,the clustering center is initialized according to the gray level characteristics of thermal faults.Then,the neighborhood pixels are clustered combining with Mediodshift clustering method.In order to promote the performance in clustering of pixels as far as possible,the adjustment strategy for neighborhood gray level is introduced.Meanwhile,in order to improve the clustering efficiency,a clustering thresholding segmentation mechanism from high to low is adopted,which enables Mediodshift algorithm to cluster the pixels of fault region quickly and to extract the thermal fault region effectively from infrared image.Finally,the effectiveness of the proposed method is verified by the experiments on some classic electronic fault images,and compared with some existing methods,which is further proved that the proposed method has better performance in fault region extraction.