电力设备红外图像缺陷检测

机器人在巡检过程中采集到的红外图像很难反映设备目标的纹理信息。人工方法或传统机器学习方法不能精准识别和分类电力设备缺陷，同时其他环境因素容易导致误判。采用CenterNet结合结构化定位的算法模型，通过对现场红外图像数据样本收集、训练及验证算法模型的计算，实现从复杂的红外图像中以较高的准确率将不同变电站设备及其部件识别定位出来。根据设备部件表面温度范围值和识别定位出的变电站设备类型，结合相关温度规范实现电力设备红外图像缺陷检测。实验结果表明，该方法提高了电力设备红外图像缺陷检测的检测精度，为电力设备红外图像智能检测提供了新的思路。

Defect Detection of Power Equipment by Infrared Image

The infrared image collected by the robot during inspection is hard to reflect the texture information of the equipment target. The artificial methods or traditional machine learning methods cannot accurately identify and classify the defects of power equipment, and other environmental factors may easily lead to false judgment. In this paper, the algorithm model of CenterNet combined with structured positioning is adopted. Through collecting field infrared image data samples, the algorithm model is trained and verified to identify and position different substation equipment and its components with high accuracy from complex infrared images. According to the surface temperature range of equipment components and the type of substation equipment, the infrared image is combined with relevant temperature specifications to realize the defect detection of power equipment. The experimental results show that this method improves the accuracy of infrared image for detecting the defects of power equipment, and provides a new idea for infrared image used for intelligent detection of power equipment.