A Study on Location of License Plate and Feature Recognition of Vehicles

Location of license plate and recognition of vehicles are main issues in intelligent traffic management.Through image preprocessing and morphology, the locations of license plates are roughly recognized, which then be filtered to obtain the accurate locations.A recognition process for characters on the plates is completed by utilizing a neural network.Transfer Learning is used for recognition of vehicles, and a deep feature vector is developed by using an AlexNet Convolutional neural network.Morphology can be used to process poor quality of grey background and guarantee accuracy of recognition.Compared with a direct classification of image features, the classification accuracy of the deep feature vectors constructed by Transfer Learning is 85.13%, which increases by 38%.It verifies the effectiveness of Transfer Learning and image attributes that characterized by deep features based on the KNN algorithm.However, it takes time when re-extracting features and training samples for new data sets.This method is found to have the equal accuracy of classification when it is compared with Transfer Learning and AlexNet framework, which proves the robustness of Transfer Learning.