Dataset Ninja LogoDataset Ninja:

PVDN Urban Dataset

146881921
Tagself-driving
Taskinstance segmentation
Release YearMade in 2023
Licenseunknown

Introduction #

Lukas Ewecker

Continuing from the original PVDN dataset, the PVDN Urban: Provident Vehicle Detection at Night in Urban Scenarios dataset comprises over 14,000 images across 140 scenes recorded at night in two medium-sized cities in Germany. Automotive-grade RYCy cameras were employed for data collection. This dataset is specifically designed for investigating the challenge of detecting oncoming vehicles in urban scenarios during nighttime, even before they become directly visible. Each scene in the dataset includes annotations of light reflections caused by oncoming vehicles, marked using binary masks.

ExpandExpand
Dataset LinkHomepage

Summary #

PVDN Urban: Provident Vehicle Detection at Night in Urban Scenarios is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the automotive industry.

The dataset consists of 14688 images with 33545 labeled objects belonging to 1 single class (reflection).

Images in the PVDN Urban dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. There are 7059 (48% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (11054 images), val (2148 images), and test (1486 images). Alternatively, the dataset could be split into 2 tags: contains annotations (7756 images) and oncoming vehicle visible (4024 images). The dataset was released in 2023.

Dataset Poster

Explore #

PVDN Urban dataset has 14688 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 PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
OpenSample annotation mask from PVDN UrbanSample image from PVDN Urban
πŸ‘€
Have a look at 14688 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
reflectionβž”
mask
7629
33545
4.4
0.52%

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
reflection
mask
33545
0.12%
11%
0%
3px
0.28%
653px
60.46%
41px
3.8%
3px
0.16%
1373px
71.51%

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 33545 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 33545
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”
reflection
mask
rosbag2_2023_04_28-19_16_53_front_wide_95.jpg
1080 x 1920
41px
3.8%
47px
2.45%
0.06%
2βž”
reflection
mask
rosbag2_2023_04_28-19_24_57_front_narrow_77.jpg
1080 x 1920
69px
6.39%
264px
13.75%
0.34%
3βž”
reflection
mask
rosbag2_2023_04_28-19_18_14_front_left_60.jpg
1080 x 1920
31px
2.87%
907px
47.24%
0.46%
4βž”
reflection
mask
rosbag2_2023_04_24-19_30_55_front_narrow_105.jpg
1080 x 1920
35px
3.24%
300px
15.62%
0.19%
5βž”
reflection
mask
rosbag2_2023_05_05-19_34_38_front_wide_60.jpg
1080 x 1920
64px
5.93%
447px
23.28%
0.59%
6βž”
reflection
mask
rosbag2_2023_05_05-19_34_38_front_wide_60.jpg
1080 x 1920
40px
3.7%
57px
2.97%
0.04%
7βž”
reflection
mask
rosbag2_2023_05_10-19_56_01_front_narrow_83.jpg
1080 x 1920
17px
1.57%
7px
0.36%
0%
8βž”
reflection
mask
rosbag2_2023_05_10-19_56_01_front_narrow_83.jpg
1080 x 1920
37px
3.43%
41px
2.14%
0.05%
9βž”
reflection
mask
rosbag2_2023_05_10-19_56_01_front_narrow_83.jpg
1080 x 1920
79px
7.31%
203px
10.57%
0.24%
10βž”
reflection
mask
rosbag2_2023_05_10-19_50_03_front_narrow_76.jpg
1080 x 1920
21px
1.94%
44px
2.29%
0.03%

License #

License is unknown for the PVDN Urban: Provident Vehicle Detection at Night in Urban Scenarios dataset.

Source

Citation #

If you make use of the PVDN Urban data, please cite the following reference:

@dataset{PVDN Urban,
  author={Lukas Ewecker},
  title={PVDN Urban: Provident Vehicle Detection at Night in Urban Scenarios},
  year={2023},
  url={https://www.kaggle.com/datasets/lukasewecker/pvdn-urban}
}

Source

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

@misc{ visualization-tools-for-pvdn-urban-dataset,
  title = { Visualization Tools for PVDN Urban Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/pvdn-urban } },
  url = { https://datasetninja.com/pvdn-urban },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Please visit dataset homepage to download the data.

. . .

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.