Traffic Vehicles Object Detection

Introduction #

Saumya Patel

The Traffic Vehicles Object Detection dataset is a valuable resource containing 1,201 images capturing the dynamic world of traffic, featuring 11,134 meticulously labeled objects. These objects are classified into seven distinct categories, including common vehicles like car, two_wheeler, as well as blur_number_plate, and other essential elements such as auto, number_plate, bus, and truck. The dataset’s origins lie in the collection of training images from traffic scenes and CCTV footage, followed by precise object annotation and labeling, making it an ideal tool for object detection tasks in the realm of transportation and surveillance.

The dataset contains labeled images of transport vehicles and number plates using LabelImg in YOLOv5 format. Author first collected some 1000 training images of traffic, vehicles and number plates, and CCTV footage videos. Then he extracted frames from videos using OpenCV. Drew a box around each object that he want the detector to see and label each box with the object class that he would like the detector to predict. Data was collected from open-source websites.

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Homepage

Summary #

Traffic Vehicles Object Detection is a dataset for an object detection task. Possible applications of the dataset could be in the smart city and surveillance industries.

The dataset consists of 1201 images with 11134 labeled objects belonging to 7 different classes including car, two_wheeler, blur_number_plate, and other: auto, number_plate, bus, and truck.

Images in the Traffic Vehicles Object Detection dataset have bounding box annotations. There are 285 (24% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (738 images), test (278 images), and val (185 images). The dataset was released in 2021.

Here are the visualized examples for the classes:

Explore #

Traffic Vehicles Object Detection dataset has 1201 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.

Sample annotation mask from Traffic Vehicles Object Detection

Sample image from Traffic Vehicles Object Detection

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Have a look at 1201 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 7 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 1-7 of 7

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
car➔ rectangle	737	5177	7.02	16.92%
two_wheeler➔ rectangle	536	1869	3.49	6.14%
blur_number_plate➔ rectangle	534	1310	2.45	0.27%
auto➔ rectangle	413	1166	2.82	4.61%
number_plate➔ rectangle	398	653	1.64	0.32%
bus➔ rectangle	298	415	1.39	9.8%
truck➔ rectangle	294	544	1.85	14.21%

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.

Rows 1-7 of 7

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
car rectangle	5177	2.57%	81.09%	0.02%	9px	1.67%	751px	89.62%	130px	14.96%	12px	1.25%	1897px	100%
two_wheeler rectangle	1869	1.87%	79.83%	0.02%	14px	2.59%	1586px	100%	107px	17.25%	5px	0.52%	1888px	93.12%
blur_number_plate rectangle	1310	0.11%	8.93%	0%	4px	0.57%	381px	39.85%	15px	2.48%	1px	0.1%	295px	22.69%
auto rectangle	1166	1.67%	53.81%	0.02%	12px	2.22%	681px	88.13%	73px	13.3%	6px	0.62%	786px	66.55%
number_plate rectangle	653	0.2%	3.18%	0.01%	7px	0.47%	116px	10.85%	27px	2.83%	19px	1.6%	241px	36.24%
truck rectangle	544	7.72%	91.73%	0.03%	11px	2.04%	2823px	100%	224px	27.07%	14px	1.46%	3565px	96.44%
bus rectangle	415	7.06%	93.5%	0.13%	16px	2.65%	1481px	99.44%	185px	22.75%	22px	3.6%	2862px	99.87%

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 11134 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 11134

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➔	car rectangle	Video2_2.jpg	540 x 960	35px	6.48%	97px	10.1%	0.65%
2➔	car rectangle	Video2_2.jpg	540 x 960	99px	18.33%	93px	9.69%	1.78%
3➔	car rectangle	Video2_2.jpg	540 x 960	34px	6.3%	63px	6.56%	0.41%
4➔	blur_number_plate rectangle	Video2_2.jpg	540 x 960	11px	2.04%	29px	3.02%	0.06%
5➔	auto rectangle	Video2_2.jpg	540 x 960	21px	3.89%	16px	1.67%	0.06%
6➔	bus rectangle	Video2_2.jpg	540 x 960	34px	6.3%	47px	4.9%	0.31%
7➔	bus rectangle	00 (11).jpg	167 x 301	74px	44.31%	237px	78.74%	34.89%
8➔	car rectangle	Video5_29.jpg	540 x 960	79px	14.63%	130px	13.54%	1.98%
9➔	car rectangle	Video5_29.jpg	540 x 960	102px	18.89%	124px	12.92%	2.44%
10➔	car rectangle	Video5_29.jpg	540 x 960	134px	24.81%	146px	15.21%	3.77%

License #

License is unknown for the Traffic Vehicles Object Detection dataset.

Source

Citation #

If you make use of the Traffic Vehicles Object Detection data, please cite the following reference:

@dataset{Traffic Vehicles Object Detection,
  author={Saumya Patel},
  title={Traffic Vehicles Object Detection},
  year={2021},
  url={https://www.kaggle.com/datasets/saumyapatel/traffic-vehicles-object-detection}
}

Source

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

@misc{ visualization-tools-for-traffic-vehicles-object-detection-dataset,
  title = { Visualization Tools for Traffic Vehicles Object Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/traffic-vehicles-object-detection } },
  url = { https://datasetninja.com/traffic-vehicles-object-detection },
  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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