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Recognition method and device for mask defects, equipment and storage medium

A recognition method and mask technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as inaccurate recognition of mask defects and inability to provide mask defect recognition methods.

Active Publication Date: 2019-06-18
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a mask defect recognition method, device, equipment and storage medium, aiming to solve the problem of inaccurate mask defect recognition due to the inability of the prior art to provide an effective mask defect recognition method

Method used

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  • Recognition method and device for mask defects, equipment and storage medium
  • Recognition method and device for mask defects, equipment and storage medium
  • Recognition method and device for mask defects, equipment and storage medium

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Embodiment 1

[0028] figure 1 The implementation process of the mask defect recognition method provided by Embodiment 1 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

[0029] In step S101, when receiving the recognition request of mask defect, obtain the mask image to be identified, and input the mask image into the pre-trained multi-feature fusion convolutional neural network model, the multi-feature fusion convolutional neural network model Including the first sub-model and the second sub-model.

[0030] The embodiments of the present invention are applicable to image data processing platforms, systems and devices, such as personal computers and servers. After obtaining the mask image to be identified, before inputting the mask image into a pre-trained multi-feature fusion convolutional neural network (Multiple-feature Fusion Convolutional Neural Network, ...

Embodiment 2

[0069] image 3 The structure of the identification device for the mask defect provided by Embodiment 2 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, including:

[0070] The mask image acquisition unit 31 is used to obtain the mask image to be identified when receiving the recognition request of the mask defect, and input the mask image into the pre-trained multi-feature fusion convolutional neural network model, and the multi-feature fusion convolution The neural network model includes a first sub-model and a second sub-model;

[0071] The space transformation processing unit 32 is used to carry out space transformation processing to the mask image by the first sub-model to obtain the corresponding first mask image;

[0072] An image dimensionality reduction processing unit 33 is used to perform dimensionality reduction processing on the mask image by the second sub-model to...

Embodiment 3

[0077] Figure 4 The structure of the identification device for the mask defect provided by the third embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, including:

[0078] The model training unit 41 is used to carry out weight parameter training to the multi-feature fusion convolutional neural network model by minimizing the preset target loss function according to the pre-built mask defect image data set, wherein the target loss function H(p, q )for x is the mask defect image in the mask defect image data set, n is the number of mask defect types, p(x) represents the probability of x expected output, and q(x) represents the probability of x's actual output;

[0079] The mask image acquisition unit 42 is used to obtain the mask image to be identified when receiving the recognition request of the mask defect, and input the mask image into the pre-trained multi-feature ...

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Abstract

The method is suitable for the technical field of machine vision image detection and deep learning, and provides a identification method and device for mask defects, equipment and storage medium. Themethod comprises the following steps of: performing spatial transformation processing on the mask image to be subjected to mask defect recognition through a first sub-model in the multi-feature fusionconvolutional neural network model; Obtaining a first mask image, performing dimension reduction processing on the mask image through a second sub-model in the multi-feature fusion convolutional neural network model; Obtaining a second mask image, carrying out image fusion on the first sub-image and the second sub-image; Obtaining a third mask image, performing classification prediction on the image features of the third mask image by adopting a Softmax function; obtaining The classification probability corresponding to the mask defect type, and identifying the mask defects of the mask imageaccording to the classification probability, so that the distinguishing degree of mask defect features is improved through the multi-feature fusion convolutional neural network model, and the accuracyof identifying different mask defect types is improved.

Description

technical field [0001] The invention belongs to the technical field of machine vision image detection and deep learning, and in particular relates to a mask defect recognition method, device, equipment and storage medium. Background technique [0002] With the continuous improvement of the economic level, the acceleration of industrial production and the increase in the use of urban vehicles, the exhaust gas and tail gas emitted by them have caused serious pollution to the environment, resulting in a serious decline in air quality and more and more serious smog. As the number of disease cases increases and the probability of infection from bacteria and germs increases, the use of masks has developed from the previous medical and industrial applications to public daily necessities. At present, the vast majority of masks are produced with non-woven fabrics. During the production process of non-woven masks, there will be poor welding of ear loops, uninstalled nose strips, diffe...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 张勇汤奇赵东宁曾庆好
Owner SHENZHEN UNIV
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