CN113191220A - Deep learning-based double-layer license plate recognition method - Google Patents
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Abstract
The invention discloses a deep learning-based double-layer license plate recognition method, which is characterized in that the double-layer license plate is recognized, and the recognized double-layer license plate is input into a constructed convolutional neural network; performing feature extraction on the double-layer license plate through a backbone network of the convolutional neural network and performing normalization processing on input data of a partial network, wherein the feature extraction backbone network comprises eight convolutional layers and four pooling layers; dividing the feature map obtained by the network calculation into an upper feature map and a lower feature map according to the height of the feature map, obtaining the feature map with the width of a first preset width by the upper feature map through mean pooling, obtaining the feature map with the width of a second preset width by the lower feature map, combining the two processed feature maps into the feature map with the width of a third preset width, obtaining an output result through a layer of full connection layer, and finally combining n output results to obtain a final double-layer license plate recognition result.
Description
Technical Field
The invention relates to the field of image recognition and machine learning, in particular to a deep learning-based double-layer license plate recognition method.
Background
License plate identification is one of important components in modern intelligent traffic systems, and is very widely applied. The method is based on technologies such as digital image processing, mode recognition and computer vision, and analyzes vehicle images or video sequences shot by a camera to obtain a unique license plate number of each vehicle, so that the recognition process is completed.
In China, various license plate formats are specified according to different vehicle types and purposes, wherein the license plates include single-layer license plates of common cars and double-layer license plates of large trucks and motorcycles.
The common existing license plate algorithm only aims at the recognition of a single-layer blue license plate of a car and cannot be directly used for recognizing a double-layer yellow license plate of a large truck or a motorcycle; and the traditional license plate recognition algorithm comprises: character segmentation and character recognition, wherein the detected license plate region needs to be subjected to character segmentation, then each character is recognized, and finally the characters are combined together. The data marking used by the method is complex to obtain, and the reasoning speed has a space for improving.
Based on the defects, the invention mainly aims at the recognition of the double-layer yellow license plate, adopts the deep learning neural network algorithm and realizes the purpose of simple, convenient and efficient double-layer license plate recognition.
Disclosure of Invention
Aiming at the technical problem, the invention constructs a lightweight convolutional neural network for extracting features, uses the global features of a double-layer license plate to be processed into one-layer feature distribution, and uses two full-connection layers to carry out global classification on license plate information. That is, the present invention is directed to solving at least one of the problems occurring in the prior art. Therefore, the invention discloses a deep learning-based double-layer license plate recognition method, which comprises the following steps of:
step 1: identifying the double-layer license plate, and inputting the identified double-layer license plate into the constructed convolutional neural network;
step 2: performing feature extraction on the double-layer license plate through a backbone network of the convolutional neural network and performing normalization processing on input data of a partial network, wherein the feature extraction backbone network comprises eight convolutional layers and four pooling layers;
and step 3: dividing the feature map obtained by the network calculation into an upper layer feature map and a lower layer feature map according to the height of the feature map, obtaining the feature map with the width of a first preset width by the upper layer feature map through mean pooling, obtaining the feature map with the width of a second preset width by the lower layer, merging the two processed feature maps into the feature map with the width of a third preset width, and processing the features of the upper layer and the lower layer into single-layer features;
and 4, step 4: the processed single-layer features with the third preset width are equally divided into n parts, each part of the evenly divided features is reconstructed to obtain a two-dimensional tensor, the output dimension is reduced to 128 through a full connection layer, then an output result is obtained through the full connection layer, and finally the n parts of the output results are combined to obtain a final double-layer license plate recognition result.
Further, convolutional neural network implementations are done using the deep learning framework PyTorch.
Further, the target loss function of the convolutional neural network adopts AM-softmax, and the formula is as follows:
and cos (theta) is the area of the calculation sample on the category, cos (theta) -m is the interval of at least a super parameter m in the area between the categories, and s (cos (theta) -m) introduces the super parameter s and expands the interval of the cos value by s times.
Still further, the step 2 further comprises: the first layer convolution to the sixth layer convolution of the feature extraction backbone network are respectively composed of 64, 128, 256, 512 and 512 convolution kernels of 3x3, the step length stride is 1, the padding is 1, the seventh layer convolution is composed of 512 convolution kernels of 3x3, the step length stride is 1, the eighth layer convolution is composed of 512 convolution kernels of 2x2, the step length stride is 1, each layer convolution adopts the following formula to normalize input data:
in the training process, a batch random gradient descending mode is adopted, wherein E [ x (k) ] refers to the average value of each batch of training data neurons x (k); the denominator is a standard deviation of the activation of each data neuron x (k).
Still further, the step 2 further comprises: the activation function used after each layer of convolution is the ReLU function:
a＝g(x)＝max(0，z)
the four pooling layers are maximum pooling layers Max padding, the size of sliding windows of the first pooling layer and the second pooling layer is 2x2, the step length stride is 2, the step length variance of an element in each window is 1, the size of sliding windows of the third pooling layer and the fourth pooling layer is 2x2, the step length stride of rightward sliding is 2, the step length stride of downward sliding is 1, and the step length variance of the element in each window is 1; the four largest pooling layers are behind the first, second, fourth, and sixth convolution layers, respectively.
Still further, the step 3 further comprises: the third preset width is the sum of the first preset width and the second preset width.
Still further, the step 3 further comprises: the first preset width takes the value of 2, and the second preset width is 5.
Furthermore, the double-layer license plate is of a double-layer structure, wherein the upper layer is a combination of one letter of a Chinese character and the lower layer is a combination of five letters and numbers.
The invention further discloses an electronic device comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the above-described deep learning-based dual-layer license plate recognition method via execution of the executable instructions.
The invention also discloses a computer readable storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to realize the deep learning-based double-layer license plate recognition method.
Compared with the prior art, the invention has the beneficial effects that: firstly, constructing a lightweight convolutional neural network for extracting features, processing the global features of a double-layer license plate into a one-layer feature distribution, and performing global classification on license plate information by using two fully-connected layers; and secondly, the purpose of reducing the class inner distance and increasing the class interval is achieved by improving the Softmax function, and the function can obtain a better effect under the condition of regularization of the features and the weight. The network uses a weighted sum mode in the training process to particularly pay attention to the problem of sample long tail distribution of the first Chinese character of the license plate.
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The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. In the drawings, like reference numerals designate corresponding parts throughout the different views.
FIG. 1 is a block diagram of the overall neural network architecture provided in one embodiment of the present invention;
FIG. 2 is a diagram of a feature extraction backbone network in one embodiment of the invention;
fig. 3 is a block diagram of an output network in one embodiment of the invention.
Detailed Description
Example one
In order to realize the purpose of simple and efficient double-layer license plate recognition, a lightweight convolutional neural network is constructed to be used for extracting features, the global features of the double-layer license plate are processed into one-layer feature distribution, and two full-connection layers are used for globally classifying license plate information. The network implementation is completed by using a deep learning framework PyTorch, the target loss function of the network adopts AM-softmax, and the formula is as follows:
cos (θ) is the area of the computed samples over the classes, cos (θ) -m is the interval between classes requiring the area to be at least over the parameter m, s*(cos (θ) -m) introduction of the hyper-parameter s, expanding the interval of cos values by a factor of s, since the interval of cos values is [0,1 ]]The value of this interval is too small to effectively distinguish the differences. After the expansion of s times, the difference of distribution can be improved, obvious Martian effect is generated, and the convergence speed is improved.
The function is based on the improvement of the Softmax function, the purpose of reducing the class inner distance and increasing the class interval is achieved, and the function can achieve a better effect under the condition of regularization of the features and the weight. The network uses a weighted sum mode in the training process to particularly pay attention to the problem of sample long tail distribution of the first Chinese character of the license plate.
The specific network structure and implementation are as follows:
the overall structure of the neural network structure is shown in fig. 1, wherein the feature extraction backbone network comprises eight convolutional layers and four pooling layers, as shown in fig. 2.
Wherein the first layer convolution to the sixth layer convolution are respectively composed of 64, 128, 256, 512 convolution kernels of 3x3 (and step size stride of 1, padding of 1), the seventh layer convolution is composed of 512 convolution kernels of 3x3 (and step size stride of 1), the eighth layer convolution is composed of 512 convolution kernels of 2x2 (and step size stride of 1), each layer convolution has a convolution using the BatchNormalization algorithm (as shown below),
the formula is used for normalizing the input data of the hierarchical network. A batch random gradient descending mode is adopted in the training process, and E [ x (k) ] refers to the average value of each batch of training data neurons x (k); the denominator is then a standard deviation of the activation of each batch of data neurons x (k).
So as to improve the robustness of the model during training, prevent overfitting and accelerate the training of the model. The activation function used after each layer of convolution is the ReLU function (shown below).
a＝g(x)＝max(0，z)
The four pooling layers are maximum pooling layers Max padding, the sliding windows of the first pooling layer and the second pooling layer are 2x2, the step length stride is 2, the step length variance of an element in each window is 1, the sliding windows of the third pooling layer and the fourth pooling layer are also 2x2, the step length stride of rightward sliding is 2, the step length stride of downward sliding is 1, and the step length variance of the element in each window is also 1; the four largest pooling layers are behind the first, second, fourth, and sixth convolution layers, respectively.
The above is a detailed composition of the backbone network, and a series of convolution characteristic graphs are output from the backbone network for the next calculation.
And then according to the characteristics of the double-layer license plate, dividing the feature graph obtained by the network calculation into an upper feature graph and a lower feature graph according to the height of the feature graph, obtaining the feature graph with the width of two by the upper feature graph through mean pooling operation, obtaining the feature graph with the width of five by the lower feature graph through the same way, combining the two feature graphs obtained by processing into the feature graph with the width of seven, and processing the features of the upper layer and the lower layer into single-layer features.
Dividing the processed single-layer features with the width of seven into seven parts, reconstructing each part of the features to obtain a two-dimensional tensor, reducing the output dimensionality to 128 through a layer of full-connection layer (adding a ReLU activation function), finally obtaining an output result through a layer of full-connection layer, and finally combining the seven output results to obtain a final seven-bit double-layer license plate recognition result. As shown in figure three.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (8)
1. A double-layer license plate recognition method based on deep learning is characterized by comprising the following steps:
step 1: identifying the double-layer license plate, and inputting the identified double-layer license plate into the constructed convolutional neural network;
step 2: performing feature extraction on the double-layer license plate through a backbone network of the convolutional neural network and performing normalization processing on input data of a partial network, wherein the feature extraction backbone network comprises eight convolutional layers and four pooling layers;
and step 3: dividing the feature map obtained by the network calculation into an upper layer feature map and a lower layer feature map according to the height of the feature map, obtaining the feature map with the width of a first preset width by the upper layer feature map through mean pooling, obtaining the feature map with the width of a second preset width by the lower layer, merging the two processed feature maps into the feature map with the width of a third preset width, and processing the features of the upper layer and the lower layer into single-layer features;
and 4, step 4: the processed single-layer features with the third preset width are equally divided into n parts, each part of the evenly divided features is reconstructed to obtain a two-dimensional tensor, the output dimension is reduced to 128 through a full connection layer, then an output result is obtained through the full connection layer, and finally the n parts of the output results are combined to obtain a final double-layer license plate recognition result.
2. The deep learning-based double-layer license plate recognition method of claim 1, wherein the convolutional neural network implementation is accomplished using a deep learning framework PyTorch.
3. The deep learning-based double-layer license plate recognition method of claim 1,
the target loss function of the convolutional neural network adopts AM-softmax, and the formula is as follows:
and cos (theta) is the area of the calculation sample on the category, cos (theta) -m is the interval of at least a super parameter m in the area between the categories, and s (cos (theta) -m) introduces the super parameter s and expands the interval of the cos value by s times.
4. The deep learning-based double-layer license plate recognition method of claim 1, wherein the step 2 further comprises: the first layer convolution to the sixth layer convolution of the feature extraction backbone network are respectively composed of 64, 128, 256, 512 and 512 convolution kernels of 3x3, the step length stride is 1, the padding is 1, the seventh layer convolution is composed of 512 convolution kernels of 3x3, the step length stride is 1, the eighth layer convolution is composed of 512 convolution kernels of 2x2, the step length stride is 1, each layer convolution adopts the following formula to normalize input data:
in the training process, a batch random gradient descending mode is adopted, wherein E [ x (k) ] refers to the average value of each batch of training data neurons x (k); the denominator is a standard deviation of the activation of each data neuron x (k).
5. The deep learning-based double-layer license plate recognition method of claim 1, wherein the step 2 further comprises: the activation function used after each layer of convolution is the ReLU function:
a＝g(x)＝max(0，z)
the four pooling layers are maximum pooling layers Max padding, the size of sliding windows of the first pooling layer and the second pooling layer is 2x2, the step length stride is 2, the step length variance of an element in each window is 1, the size of sliding windows of the third pooling layer and the fourth pooling layer is 2x2, the step length stride of rightward sliding is 2, the step length stride of downward sliding is 1, and the step length variance of the element in each window is 1; the four largest pooling layers are behind the first, second, fourth, and sixth convolution layers, respectively.
6. The deep learning-based double-layer license plate recognition method of claim 1, wherein the step 3 further comprises: the third preset width is the sum of the first preset width and the second preset width.
7. The deep learning-based double-layer license plate recognition method of claim 1, wherein the step 3 further comprises: the first preset width takes the value of 2, and the second preset width is 5.
8. The deep learning-based double-layer license plate recognition method of claim 1, wherein the double-layer license plate is of a double-layer structure, wherein the upper layer is a combination of one letter of a Chinese character and the lower layer is a combination of five letters and numbers.
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