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Image processing method and system based on service robot cloud platform

A service robot and image processing technology, applied in neural learning methods, instruments, computer parts, etc., can solve the problems of low convergence accuracy, time-consuming and labor-intensive, slow convergence speed, etc., to improve image processing ability and speed up convergence speed. , the effect of reducing the final loss

Active Publication Date: 2021-11-02
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The inventors found that when using these hand-designed optimizers to train neural networks, they often face problems such as slow convergence speed and low convergence accuracy, and it takes a lot of time and energy to adjust hyperparameters such as learning rate, which requires a lot of Model training experience is time-consuming and labor-intensive, which ultimately affects the convergence speed of image processing network training and the accuracy of processing results

Method used

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  • Image processing method and system based on service robot cloud platform
  • Image processing method and system based on service robot cloud platform
  • Image processing method and system based on service robot cloud platform

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0046] like figure 1 As shown, the present embodiment provides an image processing method based on a service robot cloud platform, which specifically includes the following steps:

[0047] Step S101: Obtain images to be classified.

[0048] Step S102: After the image to be classified is processed by the optimized image classification network model in the cloud platform of the service robot, an image classification result is obtained.

[0049] In this step S102, the optimization process of the image classification network model is:

[0050] Calculate the image classification network model gradient based on the image sample set, and normalize the gradient;

[0051] The normalized gradient is processed by the meta-optimizer system to obtain a set number of candidate updates;

[0052] Use the Look-Ahead algorithm to fuse a set number of candidate updates into a final update;

[0053] Use the final update to optimize the parameters of the training image classification network m...

Embodiment 2

[0105] This embodiment provides an image processing system based on a service robot cloud platform, which specifically includes the following modules:

[0106] Image acquisition module, which is used to acquire images to be classified;

[0107] An image classification module, which is used to process the image to be classified through the optimized image classification network model in the cloud platform of the service robot to obtain the image classification result;

[0108] The optimization process of the image classification network model is:

[0109]Calculate the image classification network model gradient based on the image sample set, and normalize the gradient;

[0110] The normalized gradient is processed by the meta-optimizer system to obtain a set number of candidate updates;

[0111] Use the Look-Ahead algorithm to fuse a set number of candidate updates into a final update;

[0112] Use the final update to optimize the parameters of the image classification netwo...

Embodiment 3

[0115] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the image processing method based on the service robot cloud platform as described in the first embodiment above are implemented.

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Abstract

The invention belongs to the field of service robot image processing, and provides an image processing method and system based on a service robot cloud platform. The method comprises the following steps: acquiring a to-be-classified image; processing a to-be-classified image by an optimized image classification network model in the service robot cloud platform to obtain an image classification result; wherein the optimization process of the image classification network model comprises the following steps: calculating the gradient of the image classification network model based on an image sample set, and normalizing the gradient; processing the normalized gradient by a meta optimizer system to obtain a set number of candidate updates; fusing a set number of candidate updates into final updates by using a Look-Ahead algorithm; and finally optimizing the parameters of the image classification network model, and storing the parameters in the service robot cloud platform.

Description

technical field [0001] The invention belongs to the field of service robot image processing, and in particular relates to an image processing method and system based on a service robot cloud platform. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Deep learning has achieved great success in fields such as image processing, and its applications are becoming more and more extensive, becoming one of the most popular technologies at present. However, the training of deep neural networks still faces many challenges. Currently, widely used optimizers are hand-designed, for example, SGD, RMSprop, AdaGrad, and Adam. Vision is the main source of information for service robots. Neural networks for image processing are widely used in service robots. Therefore, the accuracy of image processing is crucial to the performance and user experience of se...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06N3/045G06F18/241Y02T10/40
Inventor 周风余郝涛尹磊
Owner SHANDONG UNIV