# License_Plate_Detection_Pytorch
This is a two stage lightweight and robust license plate recognition in MTCNN and LPRNet using Pytorch. [MTCNN](https://arxiv.org/abs/1604.02878v1) is a very well-known real-time detection model primarily designed for human face recognition. It is modified for license plate detection. [LPRNet](https://arxiv.org/abs/1806.10447), another real-time end-to-end DNN, is utilized for the subsquent recognition. This network is attributed by its superior performance with low computational cost without preliminary character segmentation. The [Spatial Transformer Layer](https://arxiv.org/abs/1506.02025) is embeded in this work to allow a better characteristics for recognition. The recognition accuracy is up to **99%** on CCPD base dataset with ~ **80 ms/image** on Nivida Quadro P4000. Here is the illustration of the proposed pipeline:
<img src="test/pipeline.png" width="800">
## MTCNN
The modified MTCNN structure is presented as below. Only proposal net (Pnet) and output net (Onet) are used in this work since it is found that skipping Rnet will not hurt the accuracy in this case. The Onet accepts 24(height) x 94(width) BGR image which is consistent with input for LPRNet.
<img src="test/MTCNN.png" width="600" style="float: left;">
## LPRNet Performance
LPRNet coding is heavily followed by [sirius-ai](https://github.com/sirius-ai/LPRNet_Pytorch)'s repo. One exception is that the spatial transformer layer is inserted to increase the accuracy reported on CCPD database as below:
| | Base(45k) | DB | FN | Rotate | Tilt | Weather | Challenge |
| :------: | :---------: | :---------: |:---------: |:---------: |:---------: |:---------: |:---------: |
| accuracy % | 99.1 | 96.3 | 97.3 | 95.1 | 96.4 | 97.1 | 83.2 |
## Training on MTCNN
* run 'MTCNN/data_set/preprocess.py' to split training data and validation data and put in "ccpd_train" and "ccpd_val" folders respectively.
* run 'MTCNN/data_preprocessing/gen_Pnet_train_data.py', 'MTCNN/data_preprocessing/gen_Onet_train_data.py','MTCNN/data_preprocessing/assemble_Pnet_imglist.py', 'MTCNN/data_preprocessing/assemble_Onet_imglist.py' for training data preparation.
* run 'MTCNN/train/Train_Pnet.py' and 'MTCNN/train/Train_Onet.py
## Training on LPRNet
* run 'LPRNet/data/preprocess.py' to prepare the dataset
* run 'LPRNet/LPRNet_Train.py' for training
## Test
* run 'MTCNN/MTCNN.py' for license plate detection
* run 'LPRNet/LPRNet_Test.py' for license plate recognition
* run 'main.py' for both
## Reference
* [MTCNN](https://arxiv.org/abs/1604.02878v1)
* [LPRNet](https://arxiv.org/abs/1806.10447)
* [Spatial Transformer Layer](https://arxiv.org/abs/1506.02025)
**Please give me a star if it is helpful for your research**
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车牌识别LPRNet+MTCNN
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ckpt:566个
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2023-06-03
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车牌识别LPRNet+MTCNN (870个子文件)
lprnet_Iter_054900_model.ckpt 1.8MB
lprnet_Iter_015300_model.ckpt 1.8MB
lprnet_Iter_003600_model.ckpt 1.8MB
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lprnet_Iter_019800_model.ckpt 1.8MB
lprnet_Iter_025200_model.ckpt 1.8MB
lprnet_Iter_036900_model.ckpt 1.8MB
lprnet_Iter_006300_model.ckpt 1.8MB
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lprnet_Iter_045000_model.ckpt 1.8MB
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lprnet_Iter_018900_model.ckpt 1.8MB
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lprnet_Iter_016200_model.ckpt 1.8MB
lprnet_Iter_054900_model.ckpt 1.77MB
lprnet_Iter_015300_model.ckpt 1.77MB
lprnet_Iter_003600_model.ckpt 1.77MB
lprnet_Iter_024300_model.ckpt 1.77MB
lprnet_Iter_027000_model.ckpt 1.77MB
lprnet_Iter_013500_model.ckpt 1.77MB
lprnet_Iter_053100_model.ckpt 1.77MB
lprnet_Iter_019800_model.ckpt 1.77MB
lprnet_Iter_025200_model.ckpt 1.77MB
lprnet_Iter_036900_model.ckpt 1.77MB
lprnet_Iter_006300_model.ckpt 1.77MB
lprnet_Iter_037800_model.ckpt 1.77MB
lprnet_Iter_042300_model.ckpt 1.77MB
lprnet_Iter_005400_model.ckpt 1.77MB
lprnet_Iter_034200_model.ckpt 1.77MB
lprnet_Iter_041400_model.ckpt 1.77MB
lprnet_Iter_000900_model.ckpt 1.77MB
lprnet_Iter_008100_model.ckpt 1.77MB
lprnet_Iter_038700_model.ckpt 1.77MB
lprnet_Iter_002700_model.ckpt 1.77MB
lprnet_Iter_026100_model.ckpt 1.77MB
lprnet_Iter_021600_model.ckpt 1.77MB
lprnet_Iter_004500_model.ckpt 1.77MB
lprnet_Iter_043200_model.ckpt 1.77MB
lprnet_Iter_036000_model.ckpt 1.77MB
lprnet_Iter_047700_model.ckpt 1.77MB
lprnet_Iter_022500_model.ckpt 1.77MB
lprnet_Iter_012600_model.ckpt 1.77MB
lprnet_Iter_033300_model.ckpt 1.77MB
lprnet_Iter_009900_model.ckpt 1.77MB
lprnet_Iter_035100_model.ckpt 1.77MB
lprnet_Iter_031500_model.ckpt 1.77MB
lprnet_Iter_027900_model.ckpt 1.77MB
lprnet_Iter_045000_model.ckpt 1.77MB
lprnet_Iter_014400_model.ckpt 1.77MB
lprnet_Iter_018900_model.ckpt 1.77MB
lprnet_Iter_044100_model.ckpt 1.77MB
lprnet_Iter_028800_model.ckpt 1.77MB
lprnet_Iter_050400_model.ckpt 1.77MB
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