形状感知的绝缘子识别与缺陷诊断

目的 在无人机检测输电线路缺陷的研究中，为提高识别绝缘子的正确率，克服基于颜色来识别绝缘子方法的不足，依据绝缘子串的形状结构特征，研究了一种自底向上感知聚类平行线段的方法。方法 首先将在巡检图像上提取到所有方向的分段划分为6组方向线段，在每一组方向线段中，将线段长度、方向及中心点排列方向一致的线段聚类为平行线组，将平行线组合并，并整理其外接形状，结合输电线路知识模型，可靠识别绝缘子区域。为诊断玻璃绝缘子的掉片缺陷，依据计算出绝缘子的排列方向及片之间距离进行自适应分块，计算每一块的惯性矩均值特征量与惯性矩方差值特征量，依据分块之间特征量相似度来诊断是否存在掉片缺陷。结果 相比基于HSI颜色识别绝缘子的方法，识别绝缘子内部的多平行线段的结构，表现得更稳定，更适用于输电线路巡检。结论 通过无人机巡检采集的输电线路图像，实验结果验证这种方法在复杂背景的条件下能有效识别各种类型绝缘子并能检测绝缘子的掉片缺陷。

Insulator recognition and defects detection based on shape perceptual

Objective We propose the bottom-up method for the perceptual grouping of parallel lines according to the sharp structure characteristics of insulator strings in the detection of transmission line defects of unmanned aerial vehicles (UAVs). This method is applied to improve the correct recognition rate of the insulator and to overcome the deficiency to the color-based insulator recognition method. Method First, the line segments extracted from all directions are divided into six groups in the inspection image. The line segments with approximate lengths, directions, and center point orientations are then grouped into parallel segment clusters. Insulator regions are detected by combining parallel segment clusters and organizing the circumscribed shapes of these clusters based on knowledge about transmission line models. Glass insulator defects can be diagnosed according to the similarities among the feature blocks of the mean and variance of inertia moment, the adaptive partition for insulator regions, and the calculation of the direction and distance between the insulator strings. Result Compared with the HSI color-based insulator recognition method, the insulator recognition method based on multiple internal parallel line structures exhibits more stable performance and is thus more suitable for transmission line inspection. Conclusion Transmission line images from UAV inspection are tested, and results show that the proposed method can be used to identify various types of insulators and to detect insulator off-chip defects effectively in cluttered backgrounds.