Image recognition method and device based on optical neural network structure and electronic equipment

A network structure and neural network technology, applied in the field of data processing, can solve problems affecting the speed and efficiency of image recognition, and the calculation speed cannot be surpassed

Active Publication Date: 2019-06-11
SOUTH UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although they can accelerate deep learning algorithms, these hardware structures are often based on electronic components, and their calculation speed can

Method used

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  • Image recognition method and device based on optical neural network structure and electronic equipment
  • Image recognition method and device based on optical neural network structure and electronic equipment
  • Image recognition method and device based on optical neural network structure and electronic equipment

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Embodiment 1

[0036] The image recognition method based on the optical neural network structure provided by the embodiment of the present application is described below, please refer to figure 1 , the image recognition method based on the optical neural network structure in the embodiment of the present application includes:

[0037] In step 101, an image to be recognized is obtained;

[0038] In the embodiment of the present application, the image to be recognized may first be acquired by the electronic device. Optionally, if the above-mentioned electronic device is an electronic device with a shooting function such as a smart phone or a tablet computer, the camera application program of the above-mentioned electronic device may be monitored, and when it is detected that the electronic device starts the camera through the camera application program to perform a shooting operation Finally, obtain the captured picture as the image to be recognized, wherein the above-mentioned camera can be ...

Embodiment 2

[0079] An embodiment of the present invention provides an image recognition device based on an optical neural network structure, the above-mentioned optical neural network structure is composed of X-layer neural networks, and the above-mentioned X is a positive integer; please refer to Figure 5 , the above image recognition device 500 includes:

[0080] An image acquisition module 501, configured to acquire an image to be identified;

[0081] An image input module 502, configured to input the above image to be recognized to the above optical neural network structure;

[0082] A result recognition module 503, configured to determine the recognition result of the above-mentioned image to be recognized based on the output result of the above-mentioned optical neural network structure;

[0083] Among them, see Figure 6 , each layer of neural network of the above optical neural network structure includes:

[0084] The vector input unit 601 is configured to obtain the input vec...

Embodiment 3

[0102] An embodiment of the present invention provides an electronic device, please refer to Figure 7 , the electronic device in the embodiment of the present invention includes: a memory 701, one or more processors 702 ( Figure 4 only one shown in ) and a computer program stored on the memory 701 and executable on the processor. Wherein: the memory 701 is used to store software programs and modules, and the processor 702 executes various functional applications and data processing by running the software programs and units stored in the memory 701 to obtain resources corresponding to the above preset events. Specifically, the processor 702 implements the following steps by running the above-mentioned computer program stored in the memory 701:

[0103] Obtain the image to be recognized;

[0104] Inputting the above-mentioned image to be recognized into the above-mentioned optical neural network structure;

[0105] Determining the recognition result of the image to be reco...

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PUM

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Abstract

The invention discloses an image recognition method based on an optical neural network structure, an image recognition device and an electronic device, and the optical neural network structure consists of an X-layer neural network; The image recognition method comprises the steps of obtaining a to-be-recognized image; inputting the to-be-identified image into the optical neural network structure;determining a recognition result of the to-be-recognized image based on an output result of the optical neural network structure; wherein the optical neural network structure is used for obtaining aninput vector of the ith neural network for the ith neural network, and i is a positive integer greater than 0 and less than X + 1; performing linear transformation on the input vector based on Yi inner product calculation units to obtain Yi linear transformation results; activating the Yi linear transformation results through a nonlinear crystal to obtain Yi activation results; and taking the Yi activation results as output vectors of the layer of neural network. According to the scheme, a novel optical neural network structure is applied, and the image recognition speed is further increased.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an image recognition method, device and electronic equipment based on an optical neural network structure. Background technique [0002] Currently, machine learning has become a very important tool. Among them, deep learning based on deep neural network has received extensive attention and has been applied in important fields such as image recognition, speech recognition, and natural language translation. Among them, deep learning based on the traditional central processing unit (Central Processing Unit, CPU) is not the optimal solution; R&D personnel have developed a variety of hardware structures to meet the requirements of deep learning algorithms, such as graphics processing units (Graphics Processing Unit, GPU ) and Tensor Processing Unit (TPU). Although they can accelerate deep learning algorithms, these hardware structures are often based on electroni...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/02
CPCG06N3/02G06F18/00
Inventor 翁文康张笑鸣
Owner SOUTH UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA
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