Method for processing yarn spindle data, electronic device and storage medium

Patent metadata

Field	Value
Publication number	US-12586174-B2
Application number	US-202519025688-A
Country	US
Kind code	B2
Filing date	Jan 16, 2025
Priority date	Feb 4, 2024
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

A method for processing yarn spindle data, an electronic device and a storage medium, relating to the field of data processing technology, are provided. The method includes: after determining that a yarn spindle is transported into a detection area, performing a defect detection on the yarn spindle located in the detection area to obtain a target detection result of the yarn spindle. The target detection result is used to characterize a defect degree of the yarn spindle. The method further includes: after determining that the target detection result meets a preset defect value, obtaining a target level of the yarn spindle based on the target detection result of the yarn spindle and a preset level of the yarn spindle.

First claim

What is claimed is:

1 . A method for processing yarn spindle data, comprising:
based on determining that a yarn spindle is transported into a detection area, performing a defect detection on the yarn spindle located in the detection area to obtain a target detection result of the yarn spindle, wherein the target detection result characterizes a defect degree of the yarn spindle; and based on determining that the target detection result meets a preset defect value, obtaining a target level of the yarn spindle based on the target detection result of the yarn spindle and a preset level of the yarn spindle; wherein the performing the defect detection on the yarn spindle located in the detection area to obtain the target detection result of the yarn spindle, comprises: obtaining N images of the yarn spindle, wherein one image of the N images comprises at least a partial area of the yarn spindle, and N is greater than or equal to 3; inputting the N images into a target detection model to obtain a first detection result corresponding to each of the N images, wherein the target detection model is used to identify whether there are defects in a local area of the yarn spindle to obtain the first detection result, and the first detection result comprises at least one of: a quantity of defects, positions of defects, or types of defects; and obtaining the target detection result of the yarn spindle based on the first detection result corresponding to each of the N images; wherein the target detection model comprises at least a first network layer, a second network layer and a third network layer, wherein the first network layer is used to perform feature processing on an input image to obtain a low-level feature map, wherein the low-level feature map characterizes a feature map extracted after filtering out background noise in the input image, the second network layer is used to perform feature enhancement on at least key feature information in the low-level feature map to obtain a high-level feature map, and the third network layer is used to perform defect recognition based on the high-level feature map to obtain the first detection result; wherein the second network layer comprises at least a fifth sub-network layer, a sixth sub-network layer and a seventh sub-network layer, wherein the fifth sub-network layer is used to extract features from the low-level feature map, and fuse a plurality of feature maps extracted to obtain M initial fused feature maps, wherein M is an integer greater than or equal to 2, the sixth sub-network layer is used to extract key features from each of the M initial fused feature maps, and perform feature enhancement on key feature information extracted from each of the M initial fused feature maps to obtain M target enhanced feature maps, and the seventh sub-network layer is used to fuse the obtained M target enhanced feature maps to obtain the high-level feature map; wherein an i-th target enhanced feature map among the M target enhanced feature maps is obtained by: convolving an i-th initial fused feature map to obtain an i-th weight factor based on a convolution processing result; fusing an (i+1)-th initial fused feature map with the i-th initial fused feature map based on the i-th weight factor to obtain an i-th target fused feature map; extracting key features from the obtained i-th target fused feature map, performing feature enhancement on a plurality of extracted feature maps respectively to obtain a plurality of i-th initial enhanced feature maps; and fusing the plurality of i-th initial enhanced feature maps to obtain the i-th target enhanced feature map, wherein i is an integer greater than or equal to 1 and less than or equal to M−1; and based on i being equal to M, extracting key features from an M-th initial fused feature map; performing feature enhancement on a plurality of extracted feature maps respectively to obtain a plurality of M-th initial enhanced feature maps; and fusing the plurality of M-th initial enhanced feature maps to obtain an M-th target enhanced feature map.

2 . The method of claim 1 , wherein the obtaining the target detection result of the yarn spindle based on the first detection result corresponding to each of the N images, comprises:
performing at least one of following steps to obtain defect-related information of the yarn spindle, and determining the target detection result characterizing the defect degree of the yarn spindle based on the defect-related information of the yarn spindle: counting a quantity of defects in each of N first detection results to obtain a total quantity of defects of the yarn spindle; obtaining defect positions in the yarn spindle based on defect positions in each of the N first detection results; or obtaining a target defect type of the yarn spindle based on defect types in each of the N first detection results.

3 . The method of claim 2 , wherein, based on determining that the target detection result meets the preset defect value, the obtaining the target level of the yarn spindle based on the target detection result of the yarn spindle and the preset level of the yarn spindle comprises:
downgrading the preset level of the yarn spindle to obtain the target level of the detected yarn spindle based on determining that at least one of the following is satisfied: the total quantity of defects of the yarn spindle in the target detection result being greater than a preset threshold; a defect position among the defect positions in the yarn spindle in the target detection result being within a preset defect position range; or the target defect type of the yarn spindle in the target detection result being within a preset defect type range.

4 . The method of claim 1 , wherein the first network layer comprises at least a first sub-network layer, a second sub-network layer, a third sub-network layer and a fourth sub-network layer,
wherein the first sub-network layer is used to extract local features from the input image to obtain a low-frequency feature map, the second sub-network layer is used to obtain a target weight factor of the feature map of the input image, the third sub-network layer is used to extract global features from the input image to obtain a global feature map, and the fourth sub-network layer is used to fuse the low-frequency feature map and the global feature map based on the target weight factor to filter out noise and obtain the low-level feature map.

5 . An electronic device, comprising:
at least one processor; and a memory storing an instruction that, when executed by the at least one processor, causes the electronic device to: based on determining that a yarn spindle is transported into a detection area, perform a defect detection on the yarn spindle located in the detection area to obtain a target detection result of the yarn spindle, wherein the target detection result characterizes a defect degree of the yarn spindle; and based on determining that the target detection result meets a preset defect value, obtain a target level of the yarn spindle based on the target detection result of the yarn spindle and a preset level of the yarn spindle; wherein the instruction, when executed by the at least one processor, causes the electronic device to perform the defect detection on the yarn spindle located in the detection area to obtain the target detection result of the yarn spindle by: obtaining N images of the yarn spindle, wherein one image of the N images comprises at least a partial area of the yarn spindle, and N is greater than or equal to 3; inputting the N images into a target detection model to obtain a first detection result corresponding to each of the N images, wherein the target detection model is used to identify whether there are defects in a local area of the yarn spin

Assignees

• Zhejiang Hengyi Petrochemical Co Ltd
• Hangzhou Yichen Chemical Fiber Co Ltd

Inventors

• Peng Xiantao
• Wang Peng
• Qiu Yibo
• Li Dake
• Liu Mingyi
• Xu Feng
• Jin Junliang

Classifications

• G06T2207/30124

  Fabrics; Textile; Paper · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06V10/30

  Noise filtering · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V10/7715

  Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods · CPC title