Intelligent identification algorithm of adaptive feature drainage tube fault
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摘要:为了实现对高压输电线存在的故障隐患进行自动检测，本文提出了一种自适应特征引流管故障隐患智能识别算法。首先，分析了故障引流子的红外热图像特征，把故障分为两类：明显发热和微弱发热；其次，针对引流管所引起的明显发热，采用改进的Otsu阈值分割法对红外图像进行分割，运用改进的Sobel算子提取轮廓；第三，用种子填充算法分离连通域，通过Thread特征判断是否为故障引流管；最后，进入引流管所引起的微弱小区域发热识别，运用高压输电线平行特征寻找主干线区域，在主干线区域检测Harris角点，通过STWN特征判断是否为故障引流子。实验结果表明，发热隐患的识别率为94.6%，漏检率为2.2%，误识别率为5.5%。Abstract:In this paper, an intelligent recognition algorithm for hidden danger of drainage tube is presented in order to realize the automatic detection of the faults of the high voltage transmission line. First, the thermal image feature of faults is analyzed, and the faults can be divided into two types:obvious heating and weak heating. Second in view of the obvious heating caused by the drainage tube, the improved Ostu threshold segmentation method is used to implement infrared image segmentation and the improved Sobel operator is used to implment contour extraction. Third, the seed filling algorithm separation is used to connect domains, and we can determine whether the drainage tube is fault through the thread characteristics. Finally, we check the weak heating caused by the drainage tube, applying high pressure transmission line parallel features to find the region of trunk line, and then get the Harris corner around the trunk region and determine whether it is fault drainage through the STWN characteristics. Experimental results show that the successful identification rate of hidden heat fault is 94.6%, false negative rate is 2.2%, and false recognition rate is 5.5%.
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图 8直接分离与边界拓展后分离对比
Figure 8.Comparison of the images obtained by direct separation and boundary extension
图 11高压输电线路主干线区域识别
Figure 11.Main line area identification of high voltage transmission line
图 13Goldeye P/CL-008 SWIR采集红外热图像
Figure 13.Infrared thermal image acquisition with Goldeye P/CL-008 SWIR camera
图 15发热极弱、背景复杂导致引流子故障不明显
Figure 15.Drainage fault is not obvious due to the very weak heat and the complicated background
表 192幅红外图像处理结果
Table 1.Processing results obtained from 92 infrared images
设备 总图片数量 故障数 正确检测到的故障数 误检率 漏检率 引流子 92 38 33(94.6%) 5.5% 2.2% 
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