作者:曾 军，赵子根，刘景立，刘宏君，曹 磊，耿少博，万红艳
Authors:ZENG Jun, ZHAO Zi-gen, LIU Jing-li, LIU Hong-jun, CAO Lei, GENG Shao-bo, WAN Hong-yan摘要:摘 要:为找出输电线路中潜在的断股导线和表面损伤故障并防止其进一步恶化，提出了一种基于无人机巡检的输电线路导线断股和表面缺陷的识别方法。首先，通过无人机获得导线图像；然后，在通过灰度方差归一化方法（GVN）进行增强处理之后，通过自适应阈值分割来提取导线区域。其次，通过其灰度分布曲线的方波变换（SWT）检测导线断股。同时，通过导线区域的GVN图像的投影算法来识别导线表面缺陷。最后，计算断股数并分割得到可疑缺陷，获得最终的故障诊断结果。通过一系列实验分析了该技术的性能，结果表明该方法可以测量导线断股和表面缺陷故障，平均准确度分别为90.45%和92.05%。
Abstract:Abstract:In order to find out the potential strands break and damage faults and prevent its further deterioration, a recognition method of conductor break and surface defects in transmission lines′ unmanned aerial vehicle (UAV) inspection is presented in this paper. First, a conductor image is obtained by the UAV image acquisition system, and then, the conductor region is extracted by the adaptive threshold segmentation after the enhancement processing by the gray variance normalization method (GVN). Second, the conductor break is detected by the square wave transformation (SWT) of its grayscale distribution curves, which is simple and effective. Meanwhile, the conductor surface defects are identified by the projection algorithm of the GVN image of the conductor region. Finally, calculating the number of broken strands and filtering the suspect defects, the final fault diagnosis results can be obtained. We analyze the performance of the technology by a series of experiments, and the results show that the proposed method can measure the conductor break and surface defects faults with the average accuracy of 90.45% and 92.05%, respectively.

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