DOI: 10.6084/m9.figshare.24060960.v1
title: A YOLO Annotated 15-class Ground Truth Dataset for Substation Equipment

description: This dataset contains 7539 images of electric substations with 213566 annotated objects. The images were obtained using different cameras, ranging from cellphone cameras, panoramic aerial cameras, and stereo FLIR cameras, including cameras mounted on an Autonomous Guided Vehicles (AGV). A total of 15 classes of objects were identified in this dataset. These are the classes and their number of instances:<br><b>Open blade disconnect switch:</b> 1117<b>Closed blade disconnect switch:</b> 26068<b>Open tandem disconnect switch:</b> 4263<b>Closed tandem disconnect switch: </b>5402<b>Breaker: </b>4803<b>Fuse disconnect switch:</b> 1925<b>Glass disc insulator:</b> 13803<b>Porcelain pin insulator:</b> 114578<b>Muffle:</b> 8128<b>Lightning arrester:</b> 8788<b>Recloser: </b>8059<b>Power transformer:</b> 2391<b>Current transformer: </b>9293<b>Potential transformer:</b> 2904<b>Tripolar disconnect switch: </b>2044All 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:<br><b>misc:</b> This directory contains 4030 images collected manually during the morning period, ranging from 8h00 up to 12h00;<b>misc/labels: </b>This subdirectory contains the 4030 YOLO annotated .txt files for the <b>misc </b>directory;<b>agv_day:</b> This directory contains 2270 images collected using an automated guided vehicle during daytime, ranging between 8h00 and 10h00 during morning and in the afternoon between 13h00 and 17h00;<b>agv_day/labels: </b>This subdirectory contains the 2270 YOLO annotated .txt files for the <b>agv_day </b><b> </b>directory;<b>agv_night_light: </b>This directory contains 899 images collected using an automated guided vehicle during nighttime, where artificial lighting is present, ranging from 20h00 up to 21h00;<b>agv_night_light/labels: </b>This subdirectory contains the 899 YOLO annotated .txt files for the <b>agv_night_light </b><b> </b>directory;<b>agv_night_dark: </b>This directory contains 340 images collected using an automated guided vehicle during nighttime, without artificial light sources, ranging from 20h00 up to 21h00;<b>agv_night_dark/labels: </b>This subdirectory contains the 340 YOLO annotated .txt files for the <b>agv_night_dark </b><b> </b>directory;<b>classes.txt: </b>This text file lists the 15 classes indexed in the order used in the annotations.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 models for automating inspection and maintenance activities in electrical substations.