Data set of insulator defect detection based on UAV by YOLO

The dataset forwas collected by UAVs equipped with camera heads to capture images of insulators on power transmission lines. These images have a resolution of 3872×2592 pixels. A total of 488 insulator defect images were selected, and the data was annotated using the LabelMe annotation software. This study's dataset annotated four types of labels: insulator, damaged, Flashover, and hammer. The insulator is a positive class label, and damaged, Flashover, and hammer are negative class labels.

本数据集由搭载摄像头的无人机（Unmanned Aerial Vehicle, UAV）采集，用于拍摄输电线路上的绝缘子图像。该系列图像分辨率为3872×2592像素。本次研究共筛选出488张绝缘子缺陷图像，并使用LabelMe标注软件完成数据标注。本数据集共标注四类标签：绝缘子（insulator）、破损（damaged）、闪络（Flashover）以及防振锤（hammer）。其中绝缘子为正类标签，破损、闪络及防振锤均为负类标签。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于绝缘子缺陷检测的YOLO格式数据集，基于无人机采集的输电线路绝缘子图像构建，包含488张高分辨率（3872×2592像素）缺陷图像，并使用LabelMe标注了绝缘子、损坏、闪络和锤子四类标签。数据集通过水平翻转、垂直翻转等数据增强方法进行了扩展，旨在提升模型的泛化能力，适用于智能电网领域的缺陷检测任务。