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Road Damage Dataset

332142152
Tagenergy-and-utilities
Taskobject detection
Release YearMade in 2020
LicenseCC0 1.0
Download2 GB

Introduction #

Alvaro Basily

The author of the Road Damage created a dataset for detecting different road failures such as pothole, alligator crack, longitudinal crack, and lateral crack.
All images were taken with Smartphone Camera (Xiaomi Redmi Note 8) with dimensions around 1080p.

Dataset LinkHomepage

Summary #

Road Damage is a dataset for an object detection task. Possible applications of the dataset could be in the utilities industry.

The dataset consists of 3321 images with 6999 labeled objects belonging to 4 different classes including pothole, lateral crack, longitudinal crack, and other: alligator crack.

Images in the Road Damage 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 #

Road Damage dataset has 3321 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 Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
OpenSample annotation mask from Road DamageSample image from Road Damage
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Have a look at 3321 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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 1-4 of 4
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
potholeβž”
rectangle
1331
2657
2
2.06%
lateral crackβž”
rectangle
1294
2156
1.67
2.8%
longitudinal crackβž”
rectangle
892
1222
1.37
1.31%
alligator crackβž”
rectangle
790
964
1.22
2.92%

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 1-4 of 4
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
2657
1.03%
22.77%
0.01%
6px
0.56%
382px
35.37%
62px
5.78%
21px
1.09%
1617px
84.22%
lateral crack
rectangle
2156
1.72%
17.44%
0.01%
8px
0.74%
442px
40.93%
163px
15.17%
14px
0.73%
865px
45.05%
longitudinal crack
rectangle
1222
0.96%
14%
0.01%
6px
0.56%
391px
36.2%
34px
3.23%
30px
1.56%
1918px
99.9%
alligator crack
rectangle
964
2.39%
17.33%
0.03%
16px
1.48%
444px
41.11%
144px
13.97%
23px
1.2%
1138px
59.27%

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 6999 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 6999
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”
alligator crack
rectangle
BH_1020.jpeg
720 x 1280
46px
6.39%
125px
9.77%
0.62%
2βž”
alligator crack
rectangle
F_39700.jpeg
1080 x 1920
180px
16.67%
512px
26.67%
4.44%
3βž”
longitudinal crack
rectangle
Y_27360.jpeg
1080 x 1920
46px
4.26%
262px
13.65%
0.58%
4βž”
longitudinal crack
rectangle
Y_27360.jpeg
1080 x 1920
44px
4.07%
240px
12.5%
0.51%
5βž”
alligator crack
rectangle
AX_7420.jpeg
1080 x 1920
47px
4.35%
168px
8.75%
0.38%
6βž”
lateral crack
rectangle
AX_7420.jpeg
1080 x 1920
161px
14.91%
123px
6.41%
0.96%
7βž”
longitudinal crack
rectangle
AX_7420.jpeg
1080 x 1920
71px
6.57%
997px
51.93%
3.41%
8βž”
lateral crack
rectangle
C_3760.jpeg
1080 x 1920
250px
23.15%
198px
10.31%
2.39%
9βž”
alligator crack
rectangle
C_3760.jpeg
1080 x 1920
352px
32.59%
426px
22.19%
7.23%
10βž”
lateral crack
rectangle
C_3760.jpeg
1080 x 1920
154px
14.26%
107px
5.57%
0.79%

License #

Road Damage is under CC0 1.0 license.

Source

Citation #

If you make use of the Road Damage data, please cite the following reference:

@dataset{Road Damage,
	author={Alvaro Basily},
	title={Road Damage},
	year={2020},
	url={https://www.kaggle.com/datasets/alvarobasily/road-damage}
}

Source

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

@misc{ visualization-tools-for-road-damage-dataset,
  title = { Visualization Tools for Road Damage Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/road-damage } },
  url = { https://datasetninja.com/road-damage },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Road Damage 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='Road Damage', 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.

The data in original format can be downloaded here.

. . .

Disclaimer #

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