Fabric abnormal texture type identification method and device

A type recognition and abnormal technology, applied in the direction of character and pattern recognition, image data processing, instruments, etc., can solve the problems of restricting the abnormal texture type of fabrics, etc., and achieve the effect of balancing recognition difficulty and recognition efficiency, enhancing clarity and efficient recognition

Active Publication Date: 2020-04-17
湖北省纤维检验局 +1
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AI Technical Summary

Problems solved by technology

[0005] In view of the fact that existing image recognition models are difficult to adaptively and stably extract frequency-domain texture features at different levels and directions, which restricts the efficiency of stably identifying fabric abnormal texture types based on frequency-domain texture feature sets, the present invention provides a fabric abnormal texture type identification Method, device, smart device and computer readable storage medium

Method used

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  • Fabric abnormal texture type identification method and device
  • Fabric abnormal texture type identification method and device
  • Fabric abnormal texture type identification method and device

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Experimental program
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Effect test

Embodiment 1

[0029] Such as figure 1 As shown, a method for identifying abnormal texture types of fabrics includes:

[0030] Step 10: Obtain the original abnormal texture image to which the fabric belongs;

[0031] Step 20: Perform quality enhancement processing on the original abnormal texture image through a preset image enhancement model to obtain an enhanced abnormal texture image;

[0032] Step 30: Perform 6-direction decomposition transformation on the enhanced abnormal texture image at each of the N levels through the preset dual-tree complex wavelet decomposition model. After the N-level decomposition and transformation are completed, the frequency domain texture feature set is obtained. The domain texture feature set includes 2 low-frequency basic texture sub-images and 6×N high-frequency abnormal texture sub-images;

[0033] Step 40: Extract 6×N high-frequency abnormal texture sub-images from the frequency domain texture feature set;

[0034] Step 50: Perform fusion transformation on the ...

Embodiment 2

[0055] Such as figure 2 As shown, a device for identifying abnormal texture types of fabrics includes an acquisition module for acquiring the original abnormal texture image to which the fabric belongs; an enhancement module for enhancing the quality of the original abnormal texture image through a preset image enhancement model Abnormal texture image; decomposition module, used to decompose and transform the enhanced abnormal texture image in 6 directions at each of the N levels through the preset dual-tree complex wavelet decomposition model. After the N levels of decomposition and transformation are completed, the frequency is obtained Domain texture feature set; the extraction module is used to extract 6×N high-frequency abnormal texture sub-images from the frequency domain texture feature set; the fusion module is used to separately analyze the 6 of each level through the preset dual-tree complex wavelet fusion model The high-frequency abnormal texture sub-images are fused...

Embodiment 3

[0065] An intelligent device includes a memory and a processor coupled with the memory, the memory is configured to store a computer program, the processor is configured to load and execute the computer program, and the computer program is executed by the processor to realize the fabric described in the first embodiment. Any operation step performed by the abnormal texture type identification method.

[0066] The memory and the processor may be electrically connected to the communication control bus, so that the memory is coupled to the processor through the communication control bus, and smart devices such as mobile devices such as mobile phones or ipads or laptops or wearable devices.

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Abstract

The invention relates to the technical field of fabric automatic classification, and provides a fabric abnormal texture type identification method and device, and the method comprises the steps: enhancing the quality of an original abnormal texture image through a preset image enhancement model, and generating an enhanced abnormal texture image; performing multi-level multi-direction decompositionon the enhanced abnormal texture image through a preset dual-tree complex wavelet decomposition model to obtain a frequency domain texture feature set; extracting 6*N high-frequency abnormal texturesub-graphs from the frequency domain texture feature set, fusing the six high-frequency abnormal texture sub-images in each level into a high-frequency abnormal texture fusion image of the corresponding level through a preset dual-tree complex wavelet fusion model; and identifying the abnormal texture type to which the fabric belongs according to all high-frequency abnormal texture fusion images.Thus, adaptive and stable transformation in different levels and different directions is promoted to obtain a frequency domain texture feature set, and accurate and efficient identification of the abnormal texture type of the fabric according to the frequency domain texture feature set is well supported.

Description

Technical field [0001] The invention relates to the technical field of fabric automatic classification, in particular to a method and device for identifying abnormal texture types of fabrics. Background technique [0002] With the development and wide application of image recognition technology, the automatic recognition of abnormal texture types of fabrics based on images has greatly improved the degree and efficiency of automatic recognition of abnormal texture types of fabrics compared to manual methods, such as: fabric defects, hair balls, Unusual texture types such as wrinkles and structural damage. [0003] The existing automatic image recognition of fabric abnormal texture types is mainly divided into two categories: First, the airspace feature set is extracted from the original abnormal texture image to which the fabric belongs through the image recognition model based on airspace, and the fabric abnormal texture type is identified based on the airspace feature set , Image...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/52G06T5/50G06T7/40
CPCG06T7/40G06T5/50G06T2207/20221G06V10/431Y02P90/30
Inventor 徐巧林柯薇邓中民佘小燕高青松刘瀚旗陈春梅梅帆
Owner 湖北省纤维检验局
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