基于图像色彩特征融合的绝缘子污秽等级检测

针对绝缘子污秽状态非接触检测问题,提出基于可见光图像RGB(red green blue)和HSI(hue saturation intensity)空间信息特征级融合的污秽等级检测方法.利用最佳熵阈值分割法(OET)提取绝缘子盘面区域,分别在RGB和HSI色彩空间进行特征计算,根据Fisher准则进行特征选择,得到可以有效表征污秽状态的特征量,利用核主元分析(KPCA)对两个色彩空间特征的组合进行降维融合,得到三维融合特征向量,结合概率神经网络(PNN)实现污秽等级识别.实验分析表明,基于核主元分析的图像信息特征级融合能够全面地反映绝缘子污秽状态,与单独利用RGB或HSI特征进行识别相比,其准确率有显著提高,可以实现绝缘子污秽等级的有效识别,为绝缘子污闪防治提供了新的方法.

Contamination Grades Measurement of Insulators Based on Image Color Feature Fusion

An insulator contamination grades measurement method based on feature level fusion of visible image information in red green blue (RGB) and hue saturation intensity (HSI) color spaces is proposed. Optimal entropic threshold (OET) segmentation algorithm is adopted to segment insulator surface. Features of RGB and HSI color spaces are calculated separately. Meanwhile, feature selection based on Fisher criterion is applied to obtain features which have the ability to represent the contamination grades efficiently. Kernel principal component analysis (KPCA) is adopted to carry out dimensionality reduction fusion of the combination of features and obtain three dimensional fused features. Probabilistic neural network (PNN) is used to identify the contamination grades. The experimental results indicate that the feature level fusion of image information based on KPCA has capability to characterize the contamination grades comprehensively. Compared with recognition using RGB or HSI features solely, the proposed method can obtain higher recognition accuracy and realize the contamination grades recognition effectively. A new method for the prevention of pollution flashover is presented.