基于鲁棒背景检测的显著性电力线路故障识别

红外技术能有效地检测电力设备过热缺陷,具有远距离、不接触、不取样、准确、快速、直观等特点。传统的电力设备故障红外人工诊断耗时、耗力,而针对人工诊断不足提出的智能诊断其难点之一在于能否较好的获得感兴趣区域。红外图像具有强度集中、对比度低等性质,常用的分割算法用于电力设备红外图像ROI获取,其结果往往是过分割。为了加强对设备的故障检测,本论文根据电气设备站安装的固定红外摄像头全天候实时监测,并且拍摄交联电缆、电缆头、电流互感器接头、变比接头、断路器、干式变压器、支柱绝缘子等二十种相关设备的电气设备故障红外图片,再经过基于鲁棒背景检测的显著性检测对故障部位进行图像分割,结果表明图片边界连通性、背景加权对比度以及优化效果,均优于传统方法,有效避免了图像过分割问题。证实了基于鲁棒背景检测的显著性检测对故障部位进行图像分割是可行的。

Significant Power Line Fault Identification Based on Robust Background Detection

Infrared technology can effectively detect the overheating defects of power equipment. It has the characteristics of long distance, no contact, no sampling, accurate, fast, intuitive and so on. The traditional infrared artificial diagnosis of power equipment fault is time-consuming and labor-consuming, but one of the difficulties of intelligent diagnosis is whether or not to obtain the region of interest. Infrared image has the properties of concentrated intensity and low contrast. The common segmentation algorithm is used to obtain infrared image of power equipment by ROI, and the result is often over-segmentation. In order to strengthen the fault detection of the equipment, this paper is based on the fixed infrared camera installed in the electrical equipment station to monitor real-time all weather, and to film the cross-linked cable, cable head, current transformer connector, circuit breaker, dry transformer, etc. The infrared image of electrical equipment fault of 20 kinds of related equipments, such as pillar insulator, is segmented by salient detection based on robust background detection. The result shows that the image edge is connected. The background weighted contrast and the optimization effect are better than the traditional method, and the problem of image over-segmentation is avoided effectively. It is proved that the significance detection based on robust background detection is feasible for image segmentation of fault location.