Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
This repository is designed to provide an open-source dataset for license plate detection and recognition, described in 《Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline》.
The google drive link for directly downloading the whole dataset: google drive 12GB. This dataset is open-source under MIT license. Files under this git repo are sample images. More details about this dataset are avialable at ECCV 2018 paper 《Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline》.
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sample: gives 6 example pictures for each sub-dataset(blur/challenge/db/fn/np/rotate/tilt).
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rpnet: The training code for a license plate localization network and an end-to-end network which can detect the license plate bounding box and recognize the corresponding license plate number in a single forward.
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ccpd_base.zip: contains 1000 pictures which are taken from different perspectives and different distances, under different illuminations and in different.
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ccpd_blur.zip: contains 1000 pictures where pictures are blurred largely.
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ccpd_challenge.zip: contains 1000 pictures which is the most difficult benchmark for LPDR algorithm.
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ccpd_characters.zip: contains numerical and character images which is designed for training neural networks to recognize segmented character images.
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ccpd_db.zip: contains 1000 pictures where illuminations on the LP area are dark or extremely bright.
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ccpd_fn.zip: contains 1000 pictures where the distance from the LP to the shooting location is relatively far or very near.
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ccpd_np.zip: contains 1000 pictures where the car in the picture dose not own a LP.
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ccpd_rotate.zip: contains 1000 pictures with great horizontal tilt degree.
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ccpd_tilt.zip: contains 1000 pictures with both relatively great horizontal tilt degree and vertical tilt degree.
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ccpd_weather.zip: contains 1000 pictures which are taken in rainy weather.
Annotations are embedded in file name.
A sample image name is "025-95_113-154&383_386&473-386&473_177&454_154&383_363&402-0_0_22_27_27_33_16-37-15.jpg". Each name can be splited into seven fields. Those fields are explained as follows.
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Area: Area ratio of license plate area to the entire picture area.
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Tilt degree: Horizontal tilt degree and vertical tilt degree.
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Bounding box coordinates: The coordinates of the left-up and the right-bottom vertices.
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Four vertices locations: The exact (x, y) coordinates of the four vertices of LP in the whole image. These coordinates start from the right-bottom vertex.
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License plate number: Each image in CCPD has only one LP. Each LP number is comprised of a Chinese character, a letter, and five letters or numbers.
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Brightness: The brightness of the license plate region.
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Blurriness: The Blurriness of the license plate region.
Input parameters are well commented in python codes. You can increase the batchSize as long as enough GPU memory is available.
For convinence, we provide a well-trained wR2 model named "wR2.pth221" in the rpnet/ folder for easy training RPnet.
First train the localization network defined in wR2.py as follows:
python wR2.py -i [IMG FOLDERS] -b 4
After wR2 finetunes, we train the RPnet defined in rpnet.py. Please specify the variable wR2Path (the path of the well-trained wR2 model) in rpnet.py.
python rpnet.py -i [TRAIN IMG FOLDERS] -b 4 -se 0 -f [MODEL SAVE FOLDER] -t [TEST IMG FOLDERS]
After fine-tuning RPnet, you need to uncompress a zip folder and select it as the test directory. The argument after -s is a folder for storing failure cases.
python rpnetEval.py -m [MODEL PATH, like /**/fh02.pth] -i [TEST DIR] -s [FAILURE SAVE DIR]
If you have any problems about CCPD, please contact [email protected].
Please cite the paper 《Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline》, if you benefit from this dataset.