License_Plate_Detection_Pytorch

This is a two stage lightweight and robust license plate recognition in MTCNN and LPRNet using Pytorch. MTCNN is a very well-known real-time detection model primarily designed for human face recognition. It is modified for license plate detection. LPRNet, 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 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:

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.

LPRNet Performance

LPRNet coding is heavily followed by sirius-ai'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

Download the CCPD data and put it into 'ccpd' folder
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

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