Dataset Ninja LogoDataset Ninja:

Pylon Components Dataset

52232080
Tagenergy-and-utilities, drones
Taskobject detection
Release YearMade in 2021
LicenseCC BY 4.0
Download2 GB

Introduction #

Released 2021-03-02 ·Naeem Ayoub, Oscar Bowen Schofield

The Pylon Components: An Open-source Pylon Components and Fault Detection Dataset for Training the ML Algorithms serves as a valuable resource for the pylon components inspection community, offering accessibility to a diverse collection of data. It comprises 555 images, provided in both jpg and png formats, showcasing three distinct categories of pylon components: insolator, covered_insolator, and pylon. Moreover, the dataset is accompanied by annotation files containing bounding boxes for each image.

In the context of machine learning and computer vision, this dataset presents a compelling three-class classification challenge. The images encompass a wide range of dimensions, reflecting the inherent variability in the field. Furthermore, they have been captured from various angles using a drone camera, further adding to the dataset’s richness and practicality for real-world applications.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Pylon Components: An Open-source Pylon Components and Fault Detection Dataset for Training the ML Algorithms is a dataset for an object detection task. It is used in the energy industry, and in the drone inspection domain.

The dataset consists of 522 images with 1077 labeled objects belonging to 3 different classes including insolator, covered_insolator, and pylon.

Images in the Pylon Components dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021 by the SDU UAS Center, Denmark.

Dataset Poster

Explore #

Pylon Components dataset has 522 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
OpenSample annotation mask from Pylon ComponentsSample image from Pylon Components
👀
Have a look at 522 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 3 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-3 of 3
Class
Images
Objects
Count on image
average
Area on image
average
insolator
rectangle
455
877
1.93
1%
covered_insolator
rectangle
117
127
1.09
2.28%
pylon
rectangle
73
73
1
4.46%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-3 of 3
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
insolator
rectangle
877
0.52%
6.23%
0%
19px
0.84%
957px
42.53%
199px
9.07%
17px
0.42%
594px
14.85%
covered_insolator
rectangle
127
2.12%
3.58%
0.4%
201px
8.93%
669px
29.73%
472px
20.96%
180px
4.5%
605px
15.12%
pylon
rectangle
73
4.46%
12.52%
0.42%
656px
29.16%
2204px
99.72%
1725px
78.42%
58px
1.45%
483px
12.58%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 1077 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 1077
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
covered_insolator
rectangle
DJI_0288.jpg
2250 x 4000
285px
12.67%
288px
7.2%
0.91%
2
insolator
rectangle
DJI_0288.jpg
2250 x 4000
244px
10.84%
236px
5.9%
0.64%
3
insolator
rectangle
DJI_0288.jpg
2250 x 4000
226px
10.04%
255px
6.38%
0.64%
4
insolator
rectangle
frame_950.jpg
2160 x 3840
276px
12.78%
200px
5.21%
0.67%
5
covered_insolator
rectangle
DJI_0290.jpg
2250 x 4000
291px
12.93%
314px
7.85%
1.02%
6
insolator
rectangle
DJI_0290.jpg
2250 x 4000
219px
9.73%
253px
6.33%
0.62%
7
insolator
rectangle
DJI_0290.jpg
2250 x 4000
236px
10.49%
316px
7.9%
0.83%
8
insolator
rectangle
DJI_0187.jpg
2250 x 4000
451px
20.04%
285px
7.12%
1.43%
9
insolator
rectangle
frame_1578.jpg
2160 x 3840
253px
11.71%
210px
5.47%
0.64%
10
insolator
rectangle
frame_1578.jpg
2160 x 3840
131px
6.06%
157px
4.09%
0.25%

License #

Pylon Components: An Open-source Pylon Components and Fault Detection Dataset for Training the ML Algorithms is under CC BY 4.0 license.

Source

Citation #

If you make use of the Pylon Components data, please cite the following reference:

@dataset{naeem_ayoub_2021_4573988,
  author       = {Naeem Ayoub and
                  Oscar Bowen Schofield},
  title        = {{An open-source pylon components and fault 
                   detection dataset for training the ML Algorithms}},
  month        = mar,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.4573988},
  url          = {https://doi.org/10.5281/zenodo.4573988}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-pylon-components-dataset,
  title = { Visualization Tools for Pylon Components Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/pylon-components } },
  url = { https://datasetninja.com/pylon-components },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Pylon Components can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Pylon Components', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

Disclaimer #

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