PVDN Urban - Dataset Ninja

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.

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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.

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.

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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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
reflection➔ 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.

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.

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.

Rows 1-10 of 33545

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	reflection mask	rosbag2_2023_04_28-19_16_53_front_wide_95.jpg	1080 x 1920	41px	3.8%	47px	2.45%	0.06%
2➔	reflection mask	rosbag2_2023_04_28-19_24_57_front_narrow_77.jpg	1080 x 1920	69px	6.39%	264px	13.75%	0.34%
3➔	reflection mask	rosbag2_2023_04_28-19_18_14_front_left_60.jpg	1080 x 1920	31px	2.87%	907px	47.24%	0.46%
4➔	reflection mask	rosbag2_2023_04_24-19_30_55_front_narrow_105.jpg	1080 x 1920	35px	3.24%	300px	15.62%	0.19%
5➔	reflection mask	rosbag2_2023_05_05-19_34_38_front_wide_60.jpg	1080 x 1920	64px	5.93%	447px	23.28%	0.59%
6➔	reflection mask	rosbag2_2023_05_05-19_34_38_front_wide_60.jpg	1080 x 1920	40px	3.7%	57px	2.97%	0.04%
7➔	reflection mask	rosbag2_2023_05_10-19_56_01_front_narrow_83.jpg	1080 x 1920	17px	1.57%	7px	0.36%	0%
8➔	reflection mask	rosbag2_2023_05_10-19_56_01_front_narrow_83.jpg	1080 x 1920	37px	3.43%	41px	2.14%	0.05%
9➔	reflection mask	rosbag2_2023_05_10-19_56_01_front_narrow_83.jpg	1080 x 1920	79px	7.31%	203px	10.57%	0.24%
10➔	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 = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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