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（UAV）采集，用于拍摄输电线路上的绝缘子图像。该类图像的分辨率为3872×2592像素。本研究共选取488张绝缘子缺陷图像，并采用LabelMe标注软件完成数据集标注。本数据集共标注四类标签：绝缘子（insulator）、破损（damaged）、闪络（Flashover）、防振锤（hammer）。其中绝缘子为正类标签，破损、闪络与防振锤为负类标签。

搜集汇总

背景与挑战

背景概述

该数据集是一个基于无人机采集的绝缘子缺陷检测数据集，包含488张高分辨率图像（3872×2592像素），使用LabelMe软件标注了绝缘子、损坏、闪络和锤子四类标签，其中绝缘子为正类，其他为负类，适用于YOLO等目标检测算法在电力传输线路维护中的应用。