Dataset Ninja LogoDataset Ninja:

Pothole Detection Dataset

66511721
Tagsafety, self-driving
Taskobject detection
Release YearMade in 2020
LicenseDbCL v1.0
Download337 MB

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.

Dataset LinkHomepage

Summary #

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.

Dataset Poster

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.

OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
OpenSample annotation mask from Pothole DetectionSample image from Pothole Detection
πŸ‘€
Have a look at 665 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
potholeβž”
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.

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
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 1740
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”
pothole
rectangle
potholes633.png
300 x 450
120px
40%
275px
61.11%
24.44%
2βž”
pothole
rectangle
potholes633.png
300 x 450
35px
11.67%
103px
22.89%
2.67%
3βž”
pothole
rectangle
potholes21.png
300 x 450
34px
11.33%
130px
28.89%
3.27%
4βž”
pothole
rectangle
potholes21.png
300 x 450
13px
4.33%
32px
7.11%
0.31%
5βž”
pothole
rectangle
potholes21.png
300 x 450
121px
40.33%
387px
86%
34.69%
6βž”
pothole
rectangle
potholes611.png
400 x 400
67px
16.75%
91px
22.75%
3.81%
7βž”
pothole
rectangle
potholes611.png
400 x 400
43px
10.75%
65px
16.25%
1.75%
8βž”
pothole
rectangle
potholes611.png
400 x 400
110px
27.5%
124px
31%
8.53%
9βž”
pothole
rectangle
potholes327.png
300 x 442
151px
50.33%
206px
46.61%
23.46%
10βž”
pothole
rectangle
potholes583.png
400 x 400
73px
18.25%
91px
22.75%
4.15%

License #

Pothole Detection is under DbCL v1.0 license.

Source

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}
}

Source

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 = { mar },
  note = { visited on 2024-03-05 },
}

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 #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.