基于红外图像的绝缘子串自动提取和状态识别

为解决目前绝缘子低零值检测方法漏判率高、操作繁琐的问题，提出了一种新的绝缘子串红外图像中绝缘子盘面和铁帽区域自动提取方法和状态识别模型.首先将现场拍摄的绝缘子红外图像进行灰度化处理、去噪、二值化；然后从二值图像中提取反映绝缘子的特征点集合；通过特征点对二值图进行角度校正；最后通过区域提取中的特定算法提取出绝缘子的盘面和铁帽区域.通过提取该区域内的绝对温度、纹理和相对温差率作为绝缘子状态识别的特征集.将用电压分布法测得的绝缘子状态信息作为输出向量，通过训练得到优化的识别模型，用于绝缘子状态识别.该方法经过了220 kV试验验证，证实了模型的有效性和实用性.
To solve the problem of high missing rate and complicated operation in the detection methods of faulty insulator, this paper proposed a new method to automatically extract the disks and steel caps area of insulator from infrared image: via infrared image preprocessing, feature extraction, angle correction and regional extraction to achieve the purpose. This method first makes gray scene processing of insulator infrared image, noise reduction and binarization; then extracts the feature set related to insulator from the binary image to correct the angle of the binary image; finally extracts the disks and steel caps area through the specific algorithm of regional extraction. By extracting the absolute temperature region, texture and relative temperature difference as the insulator state recognition feature set, and the information obtained by measuring the voltage distribution of insulator state as output vector, the model was optimized by training. And the 220kV experiment confirms the validity and usefulness of the method.