Seriously degraded license plate image information recovery method

A license plate image and degraded image technology, applied in the application field of image processing technology, can solve the problems of inability to deal with highly degraded images and incompleteness, and achieve the effect of great application value and reasonable design.

Inactive Publication Date: 2020-11-20

TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY

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AI-Extracted Technical Summary

Problems solved by technology

With the recent success of Convolutional Neural Networks (CNN), various overall image recognition methods have emerged. For example, Goodfellow et al. proposed a CNN architecture capable of recognizing arbitrary combinations of numbers of bounded length for extracting ...

Abstract

The invention relates to a seriously degraded license plate image information recovery method. According to the method, a convolutional neural network is used to learn priori knowledge required by seriously degraded license plate information extraction; training and fine adjustment of the convolutional neural network are carried out by adopting the simulated license plate image and the real license plate image, and the character output probability of each license plate character position is obtained by adopting a SoftMax output form, so that a decision basis is provided for license plate information extraction, and unified processing of character segmentation and recognition is achieved. According to the method and the process, extraction of seriously degraded license plate information iseffectively realized, and extraction of all low-resolution license plate information is facilitated. The method can help the public security department to obtain meaningful evidences and clues, and has the maximum application value.

Application Domain

Character and pattern recognitionNeural architectures +1

Technology Topic

Computer visionInformation extraction +7

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Examples

Experimental program(1)

Example Embodiment

[0022] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0023] Step 1. Collect various license plate images in the real environment;

[0024] Step 2. Analyze the characteristics of the license plate such as font, character distribution, color characteristics, etc., and design the license plate simulation generation program. Use the license plate simulation program to generate a large number of simulated license plate images.

[0025] Step 3. Add various degrees of noise information to the simulated license plate image and the real license plate image, and generate degraded images of different sizes accordingly. Then the size of these degraded images is enlarged to the same size, and the simulated degraded image database of license plate and the degraded image database of real license plate with label information are constructed. Construct training data set, verification data set and test data set of degraded images of simulated license plates; construct training data set, verification data set and test data sets of degraded images of real license plates;

[0026] Step 4. Construct a convolutional neural network and use the simulated license plate degradation image database for training;

[0027] Step 5. Use the real license plate degradation image database to fine-tune the model obtained in step 4 to further improve the model. Finally, the model needed to restore the degraded license plate image information is obtained.

[0028] Further, the license plate image in the step 1 is deformed, corrected, and cropped to become a rectangular license plate image containing only the license plate number (excluding the first Chinese character), and the size is 560*224 pixels;

[0029] Further, the license plate simulation generation program in the step 2 also simulates the brightness, color, and background noise of the license plate to form 1 million license plate images. The generated simulation image only contains the license plate number (excluding the first Chinese character). Rectangular license plate image, the size is 560*224 pixels;

[0030] Further, adding various degrees of noise information in step 3 is adding Gaussian noise of different programs, and the signal-to-noise ratio is expressed as: -3, 0, 3, 6, 18; the width (pixels) of images of different sizes is: 10, 16, 20, 28, 40, 56. The degraded image is uniformly enlarged to 140*56 pixels. The ratio of training data set, verification data set and test data set of simulated license plate degradation image is 7:2:1. The ratio of the training data set, the verification data set and the test data set of the real license plate degraded image is 7:2:1.

[0031] Further, the structure adopted by the convolutional neural network in the step 4: two layers of convolution plus one maximized pooling layer are a group, a total of 4 groups; then two layers of fully connected layers, the last fully connected layer and 7 Two output layers are connected. Each output layer has 36 units and performs SoftMax operation as the output layer output (domestic car license plates have 7 characters except the first Chinese character, and each character can take 26 letters and 10 numbers, a total of 36 Options). The cross-entropy loss function is used for network training, the batch size is 32, and the learning rate is 0.01.

[0032] Further, in the step 5, the model obtained in step 4 is fine-tuned, and the real license plate degraded image is used for training, and the learning rate is 0.0001.

[0033] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manners. Anyone skilled in the art is based on the technology of the present invention. Other implementations derived from the solution also fall within the protection scope of the present invention.

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Classification and recommendation of technical efficacy words

high application value
reasonable design

Seriously degraded license plate image information recovery method

A license plate image and degraded image technology, applied in the application field of image processing technology, can solve the problems of inability to deal with highly degraded images and incompleteness, and achieve the effect of great application value and reasonable design.

AI-Extracted Technical Summary

Problems solved by technology

Abstract

Image

Examples

Example Embodiment

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Description & Claims & Application Information

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