A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment

This dataset contains 1660 images of electric substations with 50705 annotated objects. The images were obtained using different cameras, including cameras mounted on Autonomous Guided Vehicles (AGVs), fixed location cameras and those captured by humans using a variety of cameras. A total of 15 classes of objects were identified in this dataset. These are the classes and their number of instances:<br> <strong>Open blade disconnect switch:</strong> 310 <strong>Closed blade disconnect switch:</strong> 5243 <strong>Open tandem disconnect switch:</strong> 1599 <strong>Closed tandem disconnect switch: </strong>966 <strong>Breaker: </strong>980 <strong>Fuse disconnect switch:</strong> 355 <strong>Glass disc insulator:</strong> 3185 <strong>Porcelain pin insulator:</strong> 26499 <strong>Muffle:</strong> 1354 <strong>Lightning arrester:</strong> 1976 <strong>Recloser: </strong>2331 <strong>Power transformer:</strong> 768 <strong>Current transformer: </strong>2136 <strong>Potential transformer:</strong> 654 <strong>Tripolar disconnect switch: </strong>2349 <br> All images in this dataset were collected from a single electrical distribution substation in Brazil over a period of two years. The images were captured at various times of the day and under different weather and seasonal conditions, ensuring a diverse range of lighting conditions for the depicted objects. A team of experts in Electrical Engineering curated all the images to ensure that the angles and distances depicted in the images are suitable for automating inspections in an electrical substation. <br> The file structure of this dataset contains the following directories and files:<br> <strong>images:</strong> This directory contains 1660 electrical substation images in JPEG format. <strong>labels_json:</strong> This directory contains JSON files annotated in the VOC-style polygonal format. Each file shares the same filename as its respective image in the images directory. <strong>15_masks: </strong>This directory contains PNG segmentation masks for all 15 classes, including the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory. <strong>14_masks:</strong> This directory contains PNG segmentation masks for all classes except the porcelain pin insulator. Each file shares the same name as its corresponding image in the images directory. <strong>porcelain_masks:</strong> This directory contains PNG segmentation masks for the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory. <strong>classes.txt: </strong>This text file lists the 15 classes plus the background class used in LabelMe. <strong>json2png.py:</strong> This Python script can be used to generate segmentation masks using the VOC-style polygonal JSON annotations. <br> The dataset aims to support the development of computer vision techniques and deep learning algorithms for automating the inspection process of electrical substations. We expect it to be useful for researchers, practitioners, and engineers interested in developing and testing object detection and segmentation models for automating inspection and maintenance activities in electrical substations.

本数据集包含1660张变电站图像，共计标注50705个目标对象。图像采集自多种设备，包括搭载于自动导引车（Autonomous Guided Vehicles，AGVs）的相机、固定机位相机，以及人工使用各类相机拍摄的画面。本数据集共识别出15类目标对象，各类别及其实例数量如下：<br> <strong>常开式刀闸隔离开关：</strong> 310 <strong>常闭式刀闸隔离开关：</strong> 5243 <strong>常开式双联隔离开关：</strong> 1599 <strong>常闭式双联隔离开关：</strong> 966 <strong>断路器：</strong> 980 <strong>熔断器式隔离开关：</strong> 355 <strong>玻璃盘形绝缘子：</strong> 3185 <strong>针式瓷绝缘子：</strong> 26499 <strong>消声罩：</strong> 1354 <strong>避雷器：</strong> 1976 <strong>自动重合器：</strong> 2331 <strong>电力变压器：</strong> 768 <strong>电流互感器：</strong> 2136 <strong>电压互感器：</strong> 654 <strong>三极隔离开关：</strong> 2349 <br> 本数据集所有图像均采集自巴西某一座配电变电站，采集周期长达两年。图像拍摄于一日内的不同时段，涵盖不同天气与季节条件，确保了目标对象所处光照环境的多样性。电气工程领域的专家团队对全部图像进行了审核标注，以确保图像中的拍摄角度与拍摄距离适配变电站巡检自动化的需求。<br> 本数据集的文件结构包含以下目录与文件：<br> <strong>images：</strong> 该目录存储1660张JPEG格式的变电站图像。<strong>labels_json：</strong> 该目录存储采用VOC风格多边形标注格式的JSON标注文件，每个文件与images目录中对应的图像文件名完全一致。<strong>15_masks：</strong> 该目录存储全部15类目标的PNG格式分割掩码，包含针式瓷绝缘子类别，每个文件与images目录中对应的图像文件名完全一致。<strong>14_masks：</strong> 该目录存储除针式瓷绝缘子外其余14类目标的PNG格式分割掩码，每个文件与images目录中对应的图像文件名完全一致。<strong>porcelain_masks：</strong> 该目录存储针式瓷绝缘子类别的PNG格式分割掩码，每个文件与images目录中对应的图像文件名完全一致。<strong>classes.txt：</strong> 该文本文件列出了LabelMe中使用的15类目标与背景类别。<strong>json2png.py：</strong> 该Python脚本可基于VOC风格的多边形JSON标注文件生成分割掩码。<br> 本数据集旨在支撑面向变电站巡检自动化的计算机视觉技术与深度学习算法研发。我们期望该数据集能够为致力于开发、测试变电站巡检与运维自动化所需的目标检测与分割模型的研究人员、从业者与工程师提供助力。