摘要： 在架空输电线路中对带电状态的绝缘子进行检测和故障定位，对保证电网可靠运行具有重大意义。基于无人机平台提出一种复杂背景条件下的绝缘子检测算法。在检测算法的特征提取层引入注意力机制模块以获取更多的绝缘子特征信息，同时利用航拍图像中绝缘子的先验知识，结合K均值聚类算法改进目标候选框的生成模式。在此基础上，通过将中心损失引入绝缘子检测目标函数以增强训练过程中绝缘子类内特征的内聚性。实验结果表明，相对Faster R-CNN检测算法，在绝缘子检测数据集上Faster R-CNN改进算法检测精度提高4%以上。

关键词: 先验知识, 绝缘子检测, K均值聚类, 注意力机制, 候选框, 中心损失

Abstract: The detection and failure location of live insulators in overhead transmission lines is important to the reliable operation of power grid. Based on the Unmanned Aerial Vehicle(UAV) platform, this paper proposes an algorithm for insulator detection under complex conditions. An attention mechanism module is introduced into the feature extraction layer of the detection algorithm to obtain more information of insulator features. Then based on the prior knowledge of the insulators in aerial images, The K-means clustering algorithm is used to improve the generation mode of the object candidate box. On this basis, the center loss is introduced into the objective function of insulator detection in order to enhance the cohesion of in-class features of insulators during the training. The experimental results show that compared with the Faster R-CNN algorithm, the proposed Faster R-CNN algorithm improves the accuracy by more than 4% on the insulator detection data set. a

Key words: prior knowledge, insulator detection, K-means clustering, attention mechanism, candidate box, center loss

中图分类号: 

• TP391