Supervisely Synthetic Crack Segmentation

Introduction #

Supervisely Synthetic Crack Segmentation is a dataset for a semantic segmentation of cracks in industrial inspection. Obtaining real-world annotated data for crack segmentation can be challenging. The detailed, pixel-perfect nature of segmentation requires extensive labor and often expert knowledge, making the process time-consuming and costly. Synthetic data offers a promising solution to these challenges. It provides a controlled, cost-effective, and automated alternative to real-world data collection and manual annotation.

Learn more in the supervisely blog post.

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Summary #

Supervisely Synthetic Crack Segmentation is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the industrial domain.

The dataset consists of 1557 images with 1550 labeled objects belonging to 1 single class (cracks).

Images in the Supervisely Synthetic Crack Segmentation dataset have pixel-level semantic segmentation annotations. There are 7 (0% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: synthetic cracks (1157 images) and synthetic cracks styled (400 images). The dataset was released in 2023 by the Supervisely.

Here is the visualized example grid with animated annotations:

Explore #

Supervisely Synthetic Crack Segmentation dataset has 1557 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 Supervisely Synthetic Crack Segmentation

Sample image from Supervisely Synthetic Crack Segmentation

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Have a look at 1557 images

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

Class balance #

There are 1 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 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
cracks➔ mask	1550	1550	1	2.21%

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 1-1 of 1

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
cracks mask	1550	2.21%	10.71%	0.01%	16px	3.12%	512px	100%	351px	68.55%	19px	3.71%	512px	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 1550 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 1550

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➔	cracks mask	00_00009_5775942379_45578dea0c_o.jpg	512 x 512	415px	81.05%	289px	56.45%	1.32%
2➔	cracks mask	02_00234_4858932192_0b4761d7ef_o.jpg	512 x 512	448px	87.5%	469px	91.6%	3.1%
3➔	cracks mask	02_00393_6991918872_bb03743ab9_o.jpg	512 x 512	431px	84.18%	443px	86.52%	3.69%
4➔	cracks mask	05_00149_pexels_978462.jpeg	512 x 512	72px	14.06%	208px	40.62%	0.42%
5➔	cracks mask	00_00047_5877307859_106c6ae9dc_o.jpg	512 x 512	306px	59.77%	299px	58.4%	1.1%
6➔	cracks mask	05_00038_3192726380_9e225c1496_o.jpg	512 x 512	400px	78.12%	383px	74.8%	3.46%
7➔	cracks mask	00_00176_pexels_4604569.jpeg	512 x 512	378px	73.83%	343px	66.99%	2.17%
8➔	cracks mask	05_00008_pexels_7078272.jpeg	512 x 512	475px	92.77%	267px	52.15%	2.65%
9➔	cracks mask	04_00049_pexels_7599120.jpeg	512 x 512	364px	71.09%	368px	71.88%	1.6%
10➔	cracks mask	04_00047_5877307859_106c6ae9dc_o.jpg	512 x 512	200px	39.06%	450px	87.89%	0.74%

License #

Supervisely Synthetic Crack Segmentation is under CC BY-NC 4.0 license.

Source

Citation #

If you make use of the Supervisely Synthetic Crack Segmentation data, please cite the following reference:

@dataset{Supervisely Synthetic Crack Segmentation,
	author={Supervisely},
	title={Supervisely Synthetic Crack Segmentation},
	year={2023},
	url={https://supervisely.com/blog/introducing-supervisely-synthetic-crack-segmentation-dataset/}
}

Source

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

@misc{ visualization-tools-for-synthetic-cracks-dataset-dataset,
  title = { Visualization Tools for Supervisely Synthetic Crack Segmentation Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/synthetic-cracks-dataset } },
  url = { https://datasetninja.com/synthetic-cracks-dataset },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Supervisely Synthetic Crack Segmentation 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='Supervisely Synthetic Crack Segmentation', 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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