Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Cloth defect detection method based on multi-modal fusion deep learning

A cloth detection and defect detection technology, which is applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as cloth defects

Active Publication Date: 2020-04-17
TSINGHUA UNIV
View PDF4 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of existing cloth defect detection, and propose a cloth defect detection method based on multi-modal fusion deep learning

Method used

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
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Cloth defect detection method based on multi-modal fusion deep learning
  • Cloth defect detection method based on multi-modal fusion deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] A cloth defect detection method based on multi-modal fusion deep learning proposed by the present invention is described in detail as follows in conjunction with the accompanying drawings and embodiments:

[0021] see figure 1 , is an overall flowchart of the embodiment of the present invention, including the following steps:

[0022] Step 1: Establish a cloth detection data set for different types of defects

[0023] Contact the tactile sensing sensor with the surface of cloth with different defects, and collect the cloth texture images of various defects. The defects of cloth are divided into normal, structural defects and color defects. The structural defects include scraping, thinning and neps , holes, roving, creases and running needles, and the color defects include dirt, color flowers, color yarn, dye flowers, black spots, missing prints and dark lines; use the camera at the same position where the tactile perception sensor collects the cloth texture The extern...

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
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a cloth defect detection method based on multi-modal fusion deep learning. The method comprises the following steps: firstly, utilizing a tactile perception sensor to be in contact with cloth surfaces with different defect conditions, collecting cloth texture images with various defect conditions, utilizing a camera to collect corresponding cloth external images, and takingone corresponding cloth external image and one cloth texture image as a group of cloth detection data; connecting the feature extraction network and the multi-modal fusion network to construct a classification model based on multi-modal fusion deep learning, and taking cloth texture images and cloth external images in each group of collected cloth detection data as input and taking cloth defect conditions as output; training the established classification model by using the collected cloth detection data; and finally, detecting the defect condition of the cloth by utilizing the trained classification model. According to the method, visual and tactile complementary information is utilized, so that the detection accuracy and robustness can be greatly improved.

Description

technical field [0001] The invention relates to a cloth defect detection method based on multi-modal fusion deep learning, and belongs to the technical field of cloth defect detection. Background technique [0002] Cloth defect detection is an indispensable link in the cloth production process, which directly determines the value of the produced cloth. The defect of cloth refers to the fact that in the process of cloth production, due to some factors, the weaving machine has an error in the weaving process, which in turn makes the cloth have structural defects such as missing lines and threading in some parts, or dyeing occurs during the dyeing process. Unequal defects. Such cloth defects can lead to a decrease in the aesthetics and comfort of the garments made of the final cloth. [0003] At present, most domestic enterprises still use the method of human eye recognition to detect cloth defects. However, this method requires a lot of training and practical experience for ...

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
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/40G06T7/90G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06T7/40G06T7/90G06N3/08G06T2207/20084G06T2207/20081G06T2207/30124G06N3/045G06F18/2415G06F18/25G06F18/214G06V10/82G06T2207/10024G06T7/0008G06V10/774G06V10/764
Inventor 方斌孙富春龙行明张一帆刘华平
Owner TSINGHUA UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com