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CaSSeD: Real World Data Dataset

202961264
Tagself-driving
Tasksemantic segmentation
Release YearMade in 2022
Licenseunknown

Introduction #

Suvash Sharma, Lalitha Dabbiru, Chris Goodinet al.

The CaSSeD: Real World Data dataset is a part of the CaSSeD: CAVS Semantic Segmentation Dataset for Off-Road Autonomous Driving comprised of two sets of segmentation data specially targeted for the unstructured, off-road driving environment. The presented one is a real-world semantic segmentation dataset with 1700 images collected at the CAVS proving grounds, a 55-acre area with trails, hills, obstacles, and dense vegetation, similar to a forest environment. Data were collected during winter 2018 and summer 2019 using a Sekonix SF3325-100 GMSL camera mounted on the top of a Polaris Ranger crew XP100, capturing imagery at 10 frames per second. The images are semantically labeled using classes such as smooth trail, rough trail, small vegetation, forest, sky, and obstacles.

Please note, that the actual number of images differs from the the claimed by the authors

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Dataset LinkHomepage

Summary #

CaSSeD: CAVS Semantic Segmentation Dataset for Off-Road Autonomous Driving (Real World Data) is a dataset for a semantic segmentation task. It is used in the automotive industry.

The dataset consists of 2029 images with 9326 labeled objects belonging to 6 different classes including forest, small vegetation, smooth trail, and other: rough trail, sky, and obstacles.

Images in the CaSSeD: Real World Data dataset have pixel-level semantic segmentation annotations. There is 1 unlabeled image (i.e. without annotations). There are 8 splits in the dataset: dataset1C_main_trail (350 images), train (350 images), dataset3_north_farm (300 images), dataset4_north_slope (300 images), dataset2_fogdata (279 images), dataset1A_brown_field (200 images), test (150 images), and dataset1B_powerline (100 images). Alternatively, the dataset could be split into 3 test image set locations: browns_field (60 images), main_trail (60 images), and powerline (30 images). The dataset was released in 2022 by the Mississippi State University, Mississippi State, MS, USA and Dynetics Inc., Huntsville, AL, USA.

Dataset Poster

Explore #

CaSSeD: Real World Data dataset has 2029 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 CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
OpenSample annotation mask from CaSSeD: Real World DataSample image from CaSSeD: Real World Data
πŸ‘€
Have a look at 2029 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 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 1-6 of 6
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
forestβž”
mask
2023
2023
1
52.71%
small vegetationβž”
mask
2000
2000
1
19.58%
smooth trailβž”
mask
1680
1680
1
9.08%
rough trailβž”
mask
1505
1505
1
20.61%
skyβž”
mask
1183
1183
1
6.51%
obstaclesβž”
mask
935
935
1
1.01%

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 1-6 of 6
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
forest
mask
2023
52.71%
99.33%
2.22%
36px
7.96%
1208px
100%
515px
79.41%
410px
56.94%
1920px
100%
small vegetation
mask
2000
19.58%
58.38%
0.33%
11px
2.43%
912px
100%
325px
56.23%
88px
8.59%
1920px
100%
smooth trail
mask
1680
9.08%
75.01%
0.06%
8px
1.77%
914px
100%
302px
50.61%
9px
1.25%
1920px
100%
rough trail
mask
1505
20.61%
76.5%
0.16%
32px
4.97%
913px
100%
335px
57.41%
28px
2.73%
1920px
100%
sky
mask
1183
6.51%
50.23%
0%
3px
0.66%
1208px
100%
223px
25.79%
10px
0.98%
1920px
100%
obstacles
mask
935
1.01%
11.43%
0%
1px
0.17%
1071px
88.66%
231px
27.1%
1px
0.1%
1798px
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.

Spatial Heatmap

Objects #

Table contains all 9326 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 9326
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
smooth trail
mask
screenshot_rgba_0093.png
604 x 960
289px
47.85%
576px
60%
3.72%
2βž”
rough trail
mask
screenshot_rgba_0093.png
604 x 960
288px
47.68%
427px
44.48%
9.47%
3βž”
small vegetation
mask
screenshot_rgba_0093.png
604 x 960
304px
50.33%
960px
100%
15.66%
4βž”
forest
mask
screenshot_rgba_0093.png
604 x 960
604px
100%
960px
100%
68.09%
5βž”
sky
mask
screenshot_rgba_0093.png
604 x 960
204px
33.77%
212px
22.08%
0.94%
6βž”
smooth trail
mask
screenshot_rgba_0002.png
604 x 960
162px
26.82%
506px
52.71%
2.49%
7βž”
rough trail
mask
screenshot_rgba_0002.png
604 x 960
178px
29.47%
537px
55.94%
8.46%
8βž”
small vegetation
mask
screenshot_rgba_0002.png
604 x 960
243px
40.23%
960px
100%
10.96%
9βž”
forest
mask
screenshot_rgba_0002.png
604 x 960
574px
95.03%
960px
100%
76.04%
10βž”
obstacles
mask
screenshot_rgba_0002.png
604 x 960
39px
6.46%
54px
5.62%
0.33%

License #

License is unknown for the CaSSeD: CAVS Semantic Segmentation Dataset for Off-Road Autonomous Driving (Real World Data) dataset.

Source

Citation #

If you make use of the CaSSeD: Real World Data data, please cite the following reference:

@dataset{CaSSeD: Real World Data,
  author={Suvash Sharma and Lalitha Dabbiru and Chris Goodin and Daniel Carruth and Matthew Doude and Christopher Hudson and John Ball and Bo Tang},
  title={CaSSeD: CAVS Semantic Segmentation Dataset for Off-Road Autonomous Driving (Real World Data)},
  year={2022},
  url={https://www.cavs.msstate.edu/resources/autonomous_dataset.php}
}

Source

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

@misc{ visualization-tools-for-cassed-real-world-data-dataset,
  title = { Visualization Tools for CaSSeD: Real World Data Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/cassed-real-world-data } },
  url = { https://datasetninja.com/cassed-real-world-data },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Please visit dataset homepage to download the data.

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