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EALPR: Vehicles Dataset

208411
Tagsurveillance
Taskobject detection
Release YearMade in 2021
Licenseunknown

Introduction #

Released 2021-12-09 ·Ahmed Ramadan Youssef, Fawzya Ramadan Sayed, Abdelmgeid Ameen Ali

EALPR: Egyptian Automatic License Plate Recognition (Vehicles) stands as a specialized dataset tailored for object detection purposes, specifically focusing on the identification and localization of license plates. With a compilation of 2084 images, the dataset encompasses a total of 2140 annotated objects, all falling under the singular class of license_plate. Notably, the dataset predominantly features Egyptian License Plates (LP) marked by Arabic digits and characters, showcasing the diversity of license plate styles found across different regions and countries utilizing various scripts, including Latin letters commonly used in Europe, Brazil, the US, and several other locales. This diversity provides an extensive array of examples for training models to recognize and extract license plate information from varying international contexts.

EALPR Structure

image

EALPR: Vehicles demo.

image

EALPR: Plates demo.

Proposed method

Authors use two object detection models for license plate detection and recognition. The input to authors’ proposed system is the vehicle image, whereas the output is the recognized license plate characters and digits. Tiny-YOLOV3 is the backbone architecture for both stages LP detection and recognition.

image

Main Stages of the Proposed EALPR Approach.

About EALPR

Authors scrap the EALPR dataset images from websites such as Instagram pages and Facebook Marketplace using web scraping Tools. It has 2,450, and 12,160 characters images including many types of vehicles such as cars, buses, trucks, microbuses, and mini-busses. The vehicle images are captured using different cameras, at varying times, lighting, and background.

image

Characters and Digits Statistics in the EALPR Dataset.

The Egyptian License Plate (LP) uses the Arabic digits and characters which is varying in several countries that use Latin letters such as Europe, Brazil, US, … etc. It has a dimension 16 (height) X 40 (width) with aspect ratio 1:2 approximately. its layout is divided into 3 regions: the country region contains the Egypt word in Arabic and English format, the character region includes plate characters, and the number region contains plate digits. Egyptian License Plate structure uses 10 Arabic digits and 17 Arabic characters.

image

Egyptian License Plate Structure.

image

Plate Arabic Digits and Latin Digits.

image

Plate Arabic Characters and Latin Characters.

After collecting EALPR dataset authors manually annotate the vehicles, characters, and digits using Ybat tool.

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Dataset LinkHomepageDataset LinkResearch Paper 1 (main)Dataset LinkResearch Paper 2

Summary #

EALPR: Egyptian Automatic License Plate Recognition (Vehicles) is a dataset for an object detection task. It is used in the surveillance industry.

The dataset consists of 2084 images with 2140 labeled objects belonging to 1 single class (license_plate).

Images in the EALPR: Vehicles 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 2021 by the Fayoum University, Egypt and Minia University, Egypt.

Dataset Poster

Explore #

EALPR: Vehicles dataset has 2084 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 EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
OpenSample annotation mask from EALPR: VehiclesSample image from EALPR: Vehicles
👀
Have a look at 2084 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
Images
Objects
Count on image
average
Area on image
average
license_plate
rectangle
2084
2140
1.03
2.22%

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
license_plate
rectangle
2140
2.16%
53.78%
0.06%
14px
1.46%
529px
60%
87px
10.01%
26px
3.08%
968px
89.63%

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 2140 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 2140
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
license_plate
rectangle
1843.jpg
960 x 720
76px
7.92%
130px
18.06%
1.43%
2
license_plate
rectangle
1158.jpg
1080 x 1080
100px
9.26%
186px
17.22%
1.59%
3
license_plate
rectangle
0006.jpg
720 x 960
68px
9.44%
131px
13.65%
1.29%
4
license_plate
rectangle
0685.jpg
960 x 960
63px
6.56%
122px
12.71%
0.83%
5
license_plate
rectangle
1895.jpg
960 x 540
66px
6.88%
95px
17.59%
1.21%
6
license_plate
rectangle
0301.jpg
640 x 640
17px
2.66%
26px
4.06%
0.11%
7
license_plate
rectangle
0301.jpg
640 x 640
38px
5.94%
70px
10.94%
0.65%
8
license_plate
rectangle
0562.jpg
1512 x 1280
134px
8.86%
242px
18.91%
1.68%
9
license_plate
rectangle
0347.jpg
1125 x 900
96px
8.53%
182px
20.22%
1.73%
10
license_plate
rectangle
1592.jpg
720 x 960
89px
12.36%
203px
21.15%
2.61%

License #

License is unknown for the Egyptian Automatic License Plate Recognition: Vehicles dataset.

Source

Citation #

If you make use of the EALPR: Vehicles data, please cite the following reference:

@INPROCEEDINGS{9845514,
  author={Youssef, Ahmed Ramadan and Sayed, Fawzya Ramadan and Ali, Abdelmgeid Ameen},
  booktitle={2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)}, 
  title={A New Benchmark Dataset for Egyptian License Plate Detection and Recognition}, 
  year={2022},
  volume={},
  number={},
  pages={106-111},
  doi={10.1109/ACIRS55390.2022.9845514}
}

Source

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

@misc{ visualization-tools-for-ealpr-vehicles-dataset,
  title = { Visualization Tools for EALPR: Vehicles Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/ealpr-vehicles } },
  url = { https://datasetninja.com/ealpr-vehicles },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Please visit dataset homepage to download the data.

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