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, and the number of instances for each class is provided in the following table: Object classes and how many times they appear in the dataset. Class Instances Open blade disconnect 310 Closed blade disconnect switch 5243 Open tandem disconnect switch 1599 Closed tandem disconnect switch 966 Breaker 980 Fuse disconnect switch 355 Glass disc insulator 3185 Porcelain pin insulator 26499 Muffle 1354 Lightning arrester 1976 Recloser 2331 Power transformer 768 Current transformer 2136 Potential transformer 654 Tripolar disconnect switch 2349 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. The file structure of this dataset contains the following directories and files: images: This directory contains 1660 electrical substation images in JPEG format. images: This directory contains 1660 electrical substation images in JPEG format. labels_json: 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. 15_masks: 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. 14_masks: 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. porcelain_masks: 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. classes.txt: This text file lists the 15 classes plus the background class used in LabelMe. json2png.py: This Python script can be used to generate segmentation masks using the VOC-style polygonal JSON annotations. The dataset aims to support the development of computer vision techniques and deep learning algorithms for automating the inspection process of electrical substations. The dataset is expected 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. The authors would like to thank UTFPR for the support and infrastructure made available for the development of this research and COPEL-DIS for the support through project PD-2866-0528/2020—Development of a Methodology for Automatic Analysis of Thermal Images. We also would like to express our deepest appreciation to the team of annotators who worked diligently to produce the semantic labels for our dataset. Their hard work, dedication and attention to detail were critical to the success of this project.

本数据集包含1660幅变电站图像，共计标注50705个目标对象。图像采集设备涵盖多种类型，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，各类别实例数如下表所示：

| 目标类别 | 实例数量 |
| --- | --- |
| 敞开式闸刀隔离开关 | 310 |
| 闭合式闸刀隔离开关 | 5243 |
| 敞开式双极隔离开关 | 1599 |
| 闭合式双极隔离开关 | 966 |
| 断路器 | 980 |
| 熔断器式隔离开关 | 355 |
| 盘形玻璃绝缘子 | 3185 |
| 针式瓷绝缘子 | 26499 |
| 消声器 | 1354 |
| 避雷器 | 1976 |
| 重合器 | 2331 |
| 电力变压器 | 768 |
| 电流互感器 | 2136 |
| 电压互感器 | 654 |
| 三极隔离开关 | 2349 |

本数据集所有图像均采集自巴西某单一配电变电站，采集周期为两年。图像拍摄覆盖一日内不同时段，且包含多种天气与季节条件，确保目标对象的光照环境具备多样性。电气工程领域专家团队对所有图像进行了筛选，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需求。

本数据集的文件结构包含以下目录与文件：
- images：该目录包含1660幅JPEG格式的变电站图像。
- labels_json：该目录包含VOC格式多边形标注的JSON文件，每个文件与images目录中对应图像的文件名一致。
- 15_masks：该目录包含全部15类目标的PNG格式分割掩码，其中包含针式瓷绝缘子类别，每个文件与images目录中对应图像的文件名一致。
- 14_masks：该目录包含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目录中对应图像的文件名一致。
- porcelain_masks：该目录包含针式瓷绝缘子类别的PNG格式分割掩码，每个文件与images目录中对应图像的文件名一致。
- classes.txt：该文本文件列出了本数据集中使用的15类目标与LabelMe标注所用的背景类别。
- json2png.py：该Python脚本可通过VOC格式多边形标注JSON文件生成分割掩码。

本数据集旨在支持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开发，可服务于致力于开发、测试用于变电站巡检与运维自动化的目标检测与分割模型的研究人员、从业者与工程师。

作者感谢UTFPR为本研究提供的支持与基础设施，以及COPEL-DIS通过项目PD-2866-0528/2020——《热图像自动分析方法开发》提供的支持。同时，作者向辛勤为本数据集制作语义标注的标注团队致以最诚挚的谢意，他们的辛勤付出、专注投入与细节把控是本项目成功的关键。