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Corridor Floor Segmentation Dataset

23812444
Tagrobotics
Tasksemantic segmentation
Release YearMade in 2023
Licenseunknown

Introduction #

Raptor

The Corridor Floor Segmentation dataset is designed for the precise semantic segmentation of corridor floors with the aim of improved detection of walls and floor structures using a 2D camera-equipped mobile robot. This dataset features typical college corridor environments with numerous room entrances along the corridor, presenting a challenge for edge detection methods like Canny. The corridor floor surface exhibits a smooth texture with considerable light reflections, offering a diverse range of scenarios for analysis.

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Dataset LinkHomepage

Summary #

Corridor Floor Segmentation is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the robotics industry.

The dataset consists of 238 images with 238 labeled objects belonging to 1 single class (floor).

Images in the Corridor Floor Segmentation dataset have pixel-level semantic segmentation 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 2023.

Dataset Poster

Explore #

Corridor Floor Segmentation dataset has 238 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 Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
OpenSample annotation mask from Corridor Floor SegmentationSample image from Corridor Floor Segmentation
πŸ‘€
Have a look at 238 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 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.

Search
Rows 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
floorβž”
mask
238
238
1
46.91%

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 1-1 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
floor
mask
238
46.91%
83.79%
20.99%
240px
50%
480px
100%
356px
74.08%
221px
34.53%
640px
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 238 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 238
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”
floor
mask
frame_147.png
480 x 640
396px
82.5%
309px
48.28%
29.69%
2βž”
floor
mask
frame_042.png
480 x 640
267px
55.62%
640px
100%
35.79%
3βž”
floor
mask
frame_180.png
480 x 640
450px
93.75%
640px
100%
63.52%
4βž”
floor
mask
frame_030.png
480 x 640
420px
87.5%
640px
100%
69.14%
5βž”
floor
mask
frame_050.png
480 x 640
320px
66.67%
640px
100%
44.97%
6βž”
floor
mask
frame_193.png
480 x 640
310px
64.58%
640px
100%
44.01%
7βž”
floor
mask
frame_190.png
480 x 640
407px
84.79%
555px
86.72%
54.05%
8βž”
floor
mask
frame_188.png
480 x 640
363px
75.62%
478px
74.69%
40.21%
9βž”
floor
mask
frame_035.png
480 x 640
433px
90.21%
640px
100%
41.79%
10βž”
floor
mask
frame_010.png
480 x 640
476px
99.17%
485px
75.78%
59.95%

License #

License is unknown for the Corridor Floor Segmentation dataset.

Source

Citation #

If you make use of the Corridor Floor Segmentation data, please cite the following reference:

@dataset{Corridor Floor Segmentation,
  author={Raptor},
  title={Corridor Floor Segmentation},
  year={2023},
  url={https://www.kaggle.com/datasets/deepakmedam/corridor-floor-segmentation}
}

Source

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

@misc{ visualization-tools-for-corridor-floor-segmentation-dataset,
  title = { Visualization Tools for Corridor Floor Segmentation Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/corridor-floor-segmentation } },
  url = { https://datasetninja.com/corridor-floor-segmentation },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Please visit dataset homepage to download the data.

. . .

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