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License plate detection method based on end-to-end multi-task deep learning

A technology of license plate detection and deep learning, which is applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve the problems of low precision rate, low recall rate, missed license plate detection, etc., and achieve high detection recall rate and accuracy rate, The effect of high robustness and recall rate improvement

Active Publication Date: 2019-10-15
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] However, these methods have limitations: on the one hand, in open and natural scenes, they often fail to detect the license plate, resulting in a lower recall rate; on the other hand, due to interference factors such as image distortion and complex image background Many backgrounds with characteristics similar to the license plate will be mistakenly detected as license plates, resulting in a decrease in accuracy

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  • License plate detection method based on end-to-end multi-task deep learning
  • License plate detection method based on end-to-end multi-task deep learning
  • License plate detection method based on end-to-end multi-task deep learning

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[0022] refer to Figure 1-2 . The specific steps of the license plate detection method based on end-to-end multi-task deep learning of the present invention are as follows:

[0023] A) Training phase:

[0024] Step 1: Calculate and generate multi-task training labels from the original labels of the dataset. For each license plate image in the dataset, there is usually a one-to-one correspondence with the original annotation file. Generally speaking, the annotation file contains several sets of coordinates, each set of coordinates contains 8 values ​​and 1 note label, and the 8 values ​​correspond to the coordinates of the upper left corner (x 1 ,y 1 ), the coordinates of the upper right corner (x 2 ,y 2 ), the coordinates of the lower right corner (x 3 ,y 3 ), the coordinates of the lower left corner (x 4 ,y 4 ) (sorted clockwise), one tag information corresponds to whether the object type surrounded by the above 8 coordinate values ​​is a license plate or a vehicle....

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Abstract

The invention discloses a license plate detection method based on end-to-end multi-task deep learning. The method is used for solving the technical problem that an existing license plate detection method is low in accuracy. According to the technical scheme, features of different hierarchies of an input picture are extracted through a full convolutional neural network, then two feature merging branches are used for regression to obtain a license plate and a bounding box of a corresponding vehicle, and finally license plate detection and vehicle detection are achieved at the same time. Due to the fact that convolution characteristics of different stages are utilized, license plate detection under the variable-scale condition can be well coped with. Through multi-task deep learning, high detection recall rate and accuracy are achieved, and high robustness is still achieved in a complex scene.

Description

technical field [0001] The invention relates to a license plate detection method, in particular to a license plate detection method based on end-to-end multi-task deep learning. Background technique [0002] With the development of intelligent transportation systems, relevant researchers have proposed more and more new intelligent transportation technologies, which are widely used in smart cities, autonomous driving and other fields. License plate detection technology is an important research content of intelligent transportation systems. Its main goal is to locate and extract license plates from pictures or videos. Due to the great success of deep learning in the field of computer vision, many researchers have designed license plate detection systems based on deep learning technology in recent years. The current research on license plate detection can be divided into two categories: traditional methods and deep learning methods. [0003] Document 1 "Y.Yuan, W.Zou, Y.Zhao,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06N3/04G06N3/08
CPCG06N3/08G06V20/63G06V20/625G06N3/045
Inventor 王琦李学龙张聪
Owner NORTHWESTERN POLYTECHNICAL UNIV
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