Introduction #
Pothole Detection dataset contains 665 images with bounding box annotations provided in PASCAL VOC format for the creation of detection models and can work as POC/POV for the maintenance of roads. All annotations belong to a single class: pothole.
HomepageSummary #
Pothole Detection is a dataset for an object detection task. Possible applications of the dataset could be in the automotive and safety industries and damage detection domain.
The dataset consists of 665 images with 1740 labeled objects belonging to 1 single class (pothole).
Images in the Pothole Detection dataset have bounding box 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 2020.
Explore #
Pothole Detection dataset has 665 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 |
|---|---|---|---|---|
potholeâž” rectangle | 665 | 1740 | 2.62 | 17.57% |
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pothole rectangle | 1740 | 6.74% | 85.4% | 0% | 2px | 0.67% | 299px | 94.67% | 56px | 16.93% | 1px | 0.25% | 539px | 99.81% |
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 1740 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” | pothole rectangle | potholes633.png | 300 x 450 | 120px | 40% | 275px | 61.11% | 24.44% |
2âž” | pothole rectangle | potholes633.png | 300 x 450 | 35px | 11.67% | 103px | 22.89% | 2.67% |
3âž” | pothole rectangle | potholes21.png | 300 x 450 | 34px | 11.33% | 130px | 28.89% | 3.27% |
4âž” | pothole rectangle | potholes21.png | 300 x 450 | 13px | 4.33% | 32px | 7.11% | 0.31% |
5âž” | pothole rectangle | potholes21.png | 300 x 450 | 121px | 40.33% | 387px | 86% | 34.69% |
6âž” | pothole rectangle | potholes611.png | 400 x 400 | 67px | 16.75% | 91px | 22.75% | 3.81% |
7âž” | pothole rectangle | potholes611.png | 400 x 400 | 43px | 10.75% | 65px | 16.25% | 1.75% |
8âž” | pothole rectangle | potholes611.png | 400 x 400 | 110px | 27.5% | 124px | 31% | 8.53% |
9âž” | pothole rectangle | potholes327.png | 300 x 442 | 151px | 50.33% | 206px | 46.61% | 23.46% |
10âž” | pothole rectangle | potholes583.png | 400 x 400 | 73px | 18.25% | 91px | 22.75% | 4.15% |
License #
Citation #
If you make use of the Pothole Detection data, please cite the following reference:
@misc{make ml,
title={Potholes Dataset},
url={https://makeml.app/datasets/potholes},
journal={Make ML}
}
If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:
@misc{ visualization-tools-for-pothole-detection-dataset,
title = { Visualization Tools for Pothole Detection Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/pothole-detection } },
url = { https://datasetninja.com/pothole-detection },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { jul },
note = { visited on 2024-07-27 },
}Download #
Dataset Pothole Detection 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='Pothole Detection', 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.
Disclaimer #
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