YOLO Annotated Wind Turbine Surface Damage

Introduction #

Ashley Foster, Oscar Best, Mario Gianniet al.

A YOLO Annotated Wind Turbine Surface Damage is a dataset of wind turbine surface damage composed of images from DTU - Drone inspection images of wind turbine dataset, split into 586x371 pixel images with YOLO format annotations for Dirt and Damage. The dataset consists of 13000 images, just under 3000 of which have instances of one of the two classes.

An original DTU dataset consists of temporal inspection images for the years of 2017 and 2018 of the same ‘Nordtank’ wind turbine at DTU wind facilities in Roskilde, Denmark.

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Research Paper

Summary #

YOLO Annotated Wind Turbine Surface Damage is a dataset for an object detection task. It is used in the energy industry, and in the drone inspection domain. The dataset presented here is not the original one. Learn more on the dataset’s homepage.

The dataset consists of 13470 images with 9351 labeled objects belonging to 2 different classes including damage and dirt.

Images in the YOLO Annotated Wind Turbine Surface Damage dataset have bounding box annotations. There are 10475 (78% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2022.

Explore #

YOLO Annotated Wind Turbine Surface Damage dataset has 13470 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 YOLO Annotated Wind Turbine Surface Damage

Sample image from YOLO Annotated Wind Turbine Surface Damage

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Have a look at 13470 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
damage➔ rectangle	2527	8770	3.47	2.46%
dirt➔ rectangle	563	581	1.03	31.33%

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
damage rectangle	8770	0.73%	91.39%	0%	1px	0.27%	371px	100%	29px	7.84%	1px	0.17%	586px	100%
dirt rectangle	581	30.37%	77.65%	0.02%	8px	2.16%	371px	100%	238px	64.04%	5px	0.85%	586px	100%

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 9351 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 9351

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➔	dirt rectangle	DJI_0688_04_03.png	371 x 586	270px	72.78%	189px	32.25%	23.47%
2➔	damage rectangle	DJI_0702_04_05.png	371 x 586	35px	9.43%	34px	5.8%	0.55%
3➔	dirt rectangle	DJI_0702_04_05.png	371 x 586	149px	40.16%	67px	11.43%	4.59%
4➔	dirt rectangle	DJI_0752_03_02.png	371 x 586	186px	50.13%	586px	100%	50.13%
5➔	damage rectangle	DJI_0780_07_07.png	371 x 586	36px	9.7%	6px	1.02%	0.1%
6➔	damage rectangle	DJI_0703_05_05.png	371 x 586	32px	8.63%	32px	5.46%	0.47%
7➔	dirt rectangle	DJI_0703_05_05.png	371 x 586	190px	51.21%	84px	14.33%	7.34%
8➔	damage rectangle	DJI_0201_02_06.png	371 x 586	16px	4.31%	11px	1.88%	0.08%
9➔	damage rectangle	DJI_0201_02_06.png	371 x 586	40px	10.78%	34px	5.8%	0.63%
10➔	damage rectangle	DJI_0374_01_05.png	371 x 586	10px	2.7%	5px	0.85%	0.02%

License #

YOLO Annotated Wind Turbine Surface Damage is under CC BY-NC 4.0 license.

Source

Citation #

If you make use of the Wind Turbine Surface Damage data, please cite the following reference:

@dataset{Wind Turbine Surface Damage,
	author={Foster, Ashley and Best, Oscar and Gianni, Mario and Khan, Asiya and Collins, Kerry and Sharma, Sanjay and SHIHAVUDDIN, ASM and Chen, Xiao},
	title={YOLO Annotated Wind Turbine Surface Damage},
	year={2022},
	url={https://www.kaggle.com/datasets/ajifoster3/yolo-annotated-wind-turbines-586x371}
}

Source

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

@misc{ visualization-tools-for-yolo-annotated-wind-turbine-surface-damage-dataset,
  title = { Visualization Tools for YOLO Annotated Wind Turbine Surface Damage Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/yolo-annotated-wind-turbine-surface-damage } },
  url = { https://datasetninja.com/yolo-annotated-wind-turbine-surface-damage },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-01 },
}

Download #

Dataset YOLO Annotated Wind Turbine Surface Damage 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='YOLO Annotated Wind Turbine Surface Damage', 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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