一种基于局部特征的PCNN电力故障区域提取方法

Extraction Method of PCNN Electronic Equipment Fault Region Based on Local Feature

为有效提取红外图像中电气设备的故障或异常区域,提出一种新的红外图像故障区域提取方法。以脉冲耦合神经网络(PCNN)同步点火机理为依据,通过简化其内部参数,同时在参数优化配置下结合故障区域和非故障区域邻域边界的局部特征,设置PCNN模型迭代结束规则,从而使模型能进行自适应迭代,并获取红外图像故障区域。针对实际的红外检测图像进行实验,结果表明,与Otsu、k-means、分水岭及改进的PCNN方法相比,该方法具有较好的故障区域提取性能。

In order to extract the fault and abnormal electronic equipment in infrared image,this paper presents a new method to extract the fault region in the infrared image. Based on the mechanism of synchronize pulse of Pulse Coupled Neural Network（ PCNN）,the inner parameters are simplified partly. At the same time,the local feature of the neighborhood of fault area and non-fault area are combined with parameter optimization configuration, and the stop rule for PCNN model iteration is set, so that the model can adaptively iterate and get the infrared image fault area. According to the actual infrared detection image,experimental results show that compared with Otsu,k-means,watershed and improved PCNN methods,the proposed method has better fault area extraction performance.