Introduction #
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.
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.
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.
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.
Class ã…¤ | Images ã…¤ | Objects ã…¤ | Count on image average | Area on image average |
---|---|---|---|---|
floorâž” 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.
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.
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.
Object ID ã…¤ | Class ã…¤ | Image name click row to open | Image size height x width | Height ã…¤ | Height ã…¤ | Width ã…¤ | Width ã…¤ | Area ã…¤ |
---|---|---|---|---|---|---|---|---|
1âž” | floor mask | frame_147.png | 480 x 640 | 396px | 82.5% | 309px | 48.28% | 29.69% |
2âž” | floor mask | frame_042.png | 480 x 640 | 267px | 55.62% | 640px | 100% | 35.79% |
3âž” | floor mask | frame_180.png | 480 x 640 | 450px | 93.75% | 640px | 100% | 63.52% |
4âž” | floor mask | frame_030.png | 480 x 640 | 420px | 87.5% | 640px | 100% | 69.14% |
5âž” | floor mask | frame_050.png | 480 x 640 | 320px | 66.67% | 640px | 100% | 44.97% |
6âž” | floor mask | frame_193.png | 480 x 640 | 310px | 64.58% | 640px | 100% | 44.01% |
7âž” | floor mask | frame_190.png | 480 x 640 | 407px | 84.79% | 555px | 86.72% | 54.05% |
8âž” | floor mask | frame_188.png | 480 x 640 | 363px | 75.62% | 478px | 74.69% | 40.21% |
9âž” | floor mask | frame_035.png | 480 x 640 | 433px | 90.21% | 640px | 100% | 41.79% |
10âž” | 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.
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}
}
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 = { nov },
note = { visited on 2024-11-11 },
}
Download #
Please visit dataset homepage to download the data.
Disclaimer #
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