Wind Turbines (by Kyle Graupe)

Introduction #

Released 2023-04-22 ·Kyle Graupe

The author of the Object Detection Dataset - Wind Turbines have curated a collection of images featuring wind turbines amidst dynamic and varying backgrounds. The dataset was specifically designed to cater to drone photographers. Considering that several commercial drones, including those from DJI, come equipped with Software Development Kits (SDKs) that support programming in languages like Python, these high-quality camera-equipped drones can be effectively paired with their respective SDKs to undertake remarkable computer vision projects.

Features:

50% probability of horizontal flip
Random Gaussian blur between 0 and 3 pixels
Random exposure adjustment of between -25 and +25 percent
Pre-Split: 87% Train, 9% Validation, 4% Test
New: Bounding Box: Noise: Up to 5% of pixels

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Homepage

Summary #

Object Detection Dataset - Wind Turbines is a dataset for an object detection task. Possible applications of the dataset could be in the energy industry.

The dataset consists of 3020 images with 18289 labeled objects belonging to 2 different classes including turbine and cable tower.

Images in the Wind Turbines (by Kyle Graupe) dataset have bounding box annotations. There are 18 (1% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (2643 images), valid (247 images), and test (130 images). The dataset was released in 2023.

Explore #

Wind Turbines (by Kyle Graupe) dataset has 3020 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 Wind Turbines (by Kyle Graupe)

Sample image from Wind Turbines (by Kyle Graupe)

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Have a look at 3020 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
turbine➔ rectangle	2869	17798	6.2	24.16%
cable tower➔ rectangle	133	491	3.69	27.28%

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 1-2 of 2

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
turbine rectangle	17798	4.21%	91.43%	0%	1px	0.09%	1687px	100%	247px	23.29%	9px	0.47%	1917px	99.84%
cable tower rectangle	491	7.55%	51.18%	0.18%	77px	6.64%	1891px	99.67%	509px	38.26%	35px	1.82%	1079px	52.69%

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 18289 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 18289

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➔	turbine rectangle	windmill51_jpg.rf.5d46fabcc53fa6386958d337da633219.jpg	1080 x 1920	989px	91.57%	1917px	99.84%	91.43%
2➔	turbine rectangle	windmill52_jpg.rf.f4cd67da99ff46b09260304387574d33.jpg	1080 x 1920	835px	77.31%	309px	16.09%	12.44%
3➔	turbine rectangle	windmill52_jpg.rf.f4cd67da99ff46b09260304387574d33.jpg	1080 x 1920	340px	31.48%	139px	7.24%	2.28%
4➔	turbine rectangle	windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg	1080 x 1920	1077px	99.72%	361px	18.8%	18.75%
5➔	turbine rectangle	windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg	1080 x 1920	123px	11.39%	105px	5.47%	0.62%
6➔	turbine rectangle	windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg	1080 x 1920	73px	6.76%	82px	4.27%	0.29%
7➔	turbine rectangle	windmill32_jpg.rf.d4b9bcbcb058a757ecdea2dabaeca2c1.jpg	1080 x 1920	33px	3.06%	53px	2.76%	0.08%
8➔	turbine rectangle	windmill92_jpg.rf.90c6b6bba4aaa64f209ece0777829bb7.jpg	1080 x 1920	341px	31.57%	220px	11.46%	3.62%
9➔	turbine rectangle	windmill80_jpg.rf.e2c24b84f656663359065183a302b505.jpg	1080 x 1920	1px	0.09%	9px	0.47%	0%
10➔	turbine rectangle	windmill80_jpg.rf.e2c24b84f656663359065183a302b505.jpg	1080 x 1920	774px	71.67%	523px	27.24%	19.52%

License #

Object Detection Dataset - Wind Turbines is under CC0 1.0 license.

Source

Citation #

If you make use of the Wind Turbines (by Kyle Graupe) data, please cite the following reference:

@dataset{Wind Turbines (by Kyle Graupe),
	author={Kyle Graupe},
	title={Object Detection Dataset - Wind Turbines},
	year={2023},
	url={https://www.kaggle.com/datasets/kylegraupe/wind-turbine-image-dataset-for-computer-vision}
}

Source

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

@misc{ visualization-tools-for-wind-turbines-dataset,
  title = { Visualization Tools for Wind Turbines (by Kyle Graupe) Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/wind-turbines } },
  url = { https://datasetninja.com/wind-turbines },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Wind Turbines (by Kyle Graupe) 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='Wind Turbines (by Kyle Graupe)', 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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