Image recognition method and device and terminal equipment

An image recognition and image technology, applied in the field of image recognition, can solve the problems of low recognition rate, low robustness, and poor scalability, etc., to achieve enhanced scalability, improved robustness, improved recognition efficiency and correct rate Effect

Pending Publication Date: 2020-07-03
TCL CORPORATION
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiment of the present invention provides an image recognition method, device and terminal equipment to solve the problem of low recognition rate an

Method used

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  • Image recognition method and device and terminal equipment
  • Image recognition method and device and terminal equipment
  • Image recognition method and device and terminal equipment

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Embodiment Construction

[0039] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0040] In order to illustrate the technical solutions of the present invention, specific examples are used below to illustrate. Please refer to figure 1 , figure 1 The implementation flow of an image recognition method provided by an embodiment of the present invention is shown, and the details are as follows:

[0041] In step S101, an original image is acquired, and the original image is prepr...

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Abstract

The method is suitable for the technical field of image recognition, and provides an image recognition method and device and terminal equipment. The method includes: acquiring an original image, preprocessing the original image to obtain a first image and a second image; inputting the first image into a primary classification convolutional neural network; obtaining image category information, inputting the second image into a secondary detection convolutional neural network corresponding to the image category information; the method is advantaged in that automatic classification and image identification of the original image are realized, defect target identification efficiency and accuracy are improved, because the convolutional neural network is introduced, image identification expansibility is enhanced, and image identification robustness is improved.

Description

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Claims

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Application Information

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Owner TCL CORPORATION
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