【摘要】 针对电力设备红外检测诊断方法落后、效率低等问题，采用双层网络进行设备类型识别和结构区域划分，从而实现快速有效的诊断。首先利用R-FCN建立电力设备识别模型，利用Mask RCNN实现电力设备区域结构的识别结果自动分割，根据划分结构自动提取不同区域的最高温度，依据识别设备类型调用不同判据自动诊断设备状态。搭建红外智能诊断平台进行实验，结果表明：该方法识别准确率高、状态判断结果可靠，无需大量的故障样本，可为电力设备故障的红外智能诊断提供一种快速有效的处理方法。更多还原

【Abstract】 Aiming at the problems of backward method and low efficiency of the power equipment infrared detection and diagnosis,the double-layer network was used to identify the equipment type and to divide the equipment structure,so as to realize the fast and effect diagnosis. Firstly,the R-FCN was used to establish the identification model of electrical equipment,and the Mask RCNN was used to segment the regional structure of power equipment. The maximum temperature of different regions was extracted automatically according to the divided structure,then the equipment status could be diagnosed by criteria according to the identified equipment type. The infrared intelligent diagnosis platform was built,and the experimental results showed that the method had high recognition accuracy,reliable state judgment results,moreover it did not need a large number of fault samples,which provided a fast and effective method for infrared diagnosis of electrical equipment.更多还原