Pylon Components - Dataset Ninja

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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Research 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.

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.

Sample annotation mask from Pylon Components

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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.

Rows 1-3 of 3

Class ㅤ	Images ㅤ	Objects ㅤ	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.

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.

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.

Rows 1-10 of 1077

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
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 = { nov },
  note = { visited on 2024-11-21 },
}

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:

. . .

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