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Convolutional neural network model training method and device and computer readable storage medium

A convolutional neural network and model training technology, applied in the field of convolutional neural network model training, can solve problems such as misjudgment of target areas

Active Publication Date: 2019-09-24
BEIJING VOLCANO ENGINE TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem solved by the present disclosure is to provide a convolutional neural network model training method to at least partially solve the technical problem that the target area is misjudged in the prior art

Method used

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  • Convolutional neural network model training method and device and computer readable storage medium
  • Convolutional neural network model training method and device and computer readable storage medium
  • Convolutional neural network model training method and device and computer readable storage medium

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Experimental program
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Embodiment 1

[0094] In order to solve the technical problem of misjudgment of a target area in the prior art, an embodiment of the present disclosure provides a method for training a convolutional neural network model. like Figure 1a As shown, the convolutional neural network model training method mainly includes the following steps S11 to S12. in:

[0095] Step S11: Constructing a convolutional neural network, wherein the convolutional layer of the convolutional neural network includes a plurality of parallel convolution kernels, and each convolution kernel corresponds to a training channel.

[0096] Among them, Convolutional Neural Networks (CNN) is a type of feed-forward neural network that includes convolutional calculations and has a deep structure, mainly including an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. Also, a convolutional neural network can include multiple convolutional layers. In this paper, the convolutional neur...

Embodiment 2

[0119] In order to solve the technical problem of low accuracy rate of target area determination in the prior art, the embodiment of the present disclosure also provides a target area determination method, such as figure 2 shown, including:

[0120] S21: Obtain an image to be recognized.

[0121] Among them, the image to be recognized can be obtained in real time through the camera. Or obtain a pre-stored image to be recognized locally.

[0122] S22: Input the image to be recognized into the convolutional neural network model.

[0123] Wherein, the convolutional neural network model is trained by using the convolutional neural network model training method described in the first embodiment above, and the specific training process is referred to the first embodiment above.

[0124] S23: Predict and obtain multiple feature data through multiple training channels of the convolutional neural network model.

[0125] Among them, a training channel corresponds to predicting a fe...

Embodiment 3

[0157] In order to solve the technical problem of low accuracy rate of target area determination in the prior art, an embodiment of the present disclosure provides a convolutional neural network model training device. The device can execute the steps in the embodiment of the convolutional neural network model training method described in the first embodiment above. like image 3 As shown, the device mainly includes: a network construction module 31 and a model training module 32; wherein,

[0158] The network construction module 31 is used to construct the convolutional neural network, wherein the convolutional layer of the convolutional neural network includes a plurality of parallel convolution kernels, and each convolution kernel corresponds to a training channel;

[0159] The model training module 32 is used to input the training sample set into the convolutional neural network, and independently trains each training channel according to the training sample set until meet...

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Abstract

The invention discloses a convolutional neural network model training method and device, an electronic device and a computer readable storage medium. The method comprises: establishing a convolutional neural network, a convolutional layer of the convolutional neural network comprising a plurality of parallel convolutional cores, and each convolutional core corresponding to one training channel; inputting the training sample set into a convolutional neural network, and independently training each training channel until respective convergence conditions are met to obtain a convolutional neural network model comprising a plurality of training channels; the plurality of training channels being respectively used for predicting a plurality of feature data associated with the target area. According to the embodiment of the invention, the training sample set is trained through the plurality of parallel training channels, the convolutional neural network model obtained through training comprises a plurality of training channels, and the plurality of training channels are respectively used for predicting a plurality of feature data associated with the target area, so that more features associated with the target area can be obtained, and the target area determination accuracy can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of convolutional neural network model training, in particular to a convolutional neural network model training method, device and computer-readable storage medium. Background technique [0002] Many of the captured video images contain cars, and the images containing cars generally include license plates. Since the license plate involves privacy, it is necessary to process the license plate in the video image or use other images to cover the license plate. When processing the image containing the license plate, it is the key to recognize the license plate area in the image. [0003] For the recognition of the license plate area in the prior art, a model is generally used to judge whether each pixel in the license plate is foreground or background, wherein the foreground is the license plate area to be recognized, and the background refers to the area in the image except the license plate. But using...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06K9/62G06N3/08
CPCG06N3/08G06V20/63G06V20/625G06F18/214
Inventor 朱延东周恺卉王长虎
Owner BEIJING VOLCANO ENGINE TECH CO LTD
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