License plate recognition method and device based on deep learning

Abstract

The embodiment of the invention provides a license plate recognition method and device based on deep learning, and the method comprises the steps: obtaining a plurality of image frames containing vehicle license plate information, extracting the features of each image frame through a convolutional neural network, and obtaining the feature representation of each image frame; detecting the feature representation of each image frame through a predetermined target detection network to obtain the category and position information of the license plate detection frame of each image frame, and segmenting the license plate of each image frame to obtain a feature map of each segmented image frame; obtaining original license plate label information in each image frame, and training to obtain a license plate recognition model; and inputting a to-be-recognized picture into the license plate recognition model for license plate recognition, and recognizing to obtain license plate characters in the to-be-recognized picture. According to the method, the problem of low license plate recognition precision caused by a large inclination angle is effectively solved, the characteristics of the license plate are fully utilized, and the license plate recognition precision and recognition efficiency are greatly improved.

Description

technical field [0001] The present invention relates to the technical field of intelligent transportation, in particular to a deep learning-based license plate recognition method and device. Background technique [0002] License plate recognition technology plays an important role in many tasks such as urban traffic management, vehicle identification, parking lot fee management, and violation handling. The influence of multiple factors such as color makes license plate recognition still a challenging task. [0003] License plate recognition includes two tasks, one is the detection of the license plate position, that is, the location of the license plate area is obtained from the captured image; the other is the recognition of the license plate characters, which is to identify the visible characters in the detected license plate area. In the prior art, license plate recognition mainly includes two methods, one is a two-stage license plate recognition method, this type of met...

AI Technical Summary

Problems solved by technology

In the prior art, license plate recognition mainly includes two methods, one is a two-stage license plate recognition method, this type of method generally detects the position of the license plate first, and then recognizes the characters of the license plate on the basis of detecting the license plate. The method needs to train and tune the detection and recognition modules separately, and cannot fully utilize the highly correlated and complementary relationship between the two; the other is an end-to-end license plate recognition method, which trains the detection and recognition processes together to directly obtain the result of license plate recognition , but the existing end-to-end license plate recognition methods have the following defects: On the one hand, it is impossible to achieve a complete end-to-end training, but to train the detector first, and then load the recognizer to continue training, because the license plate recognition training Accurate license plate location is required, and the license plate location in the early iterations is often inaccurate, which also affects the effect of license plate recognition; on the other hand, due to the different shooting angles of the license plate in the real world, the license plate area on the image The position is not completely horizontal, but presented in various shapes, resulting in inaccurate license plate recognition in practical applications; on the other hand, the existing license plate output based on a fully connected network or a recurrent neural network The recognition method is highly dependent on the amount of data and requires a large amount of license plate data for learning

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