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A textile defect detection model and its training method and application

A training method and point detection technology, which is applied in the field of deep learning and computer vision, can solve the problems of low accuracy, lack of versatility, and poor real-time performance, and achieve the effects of improving accuracy, fast detection speed, and improving accuracy

Active Publication Date: 2020-05-19
HUAZHONG UNIV OF SCI & TECH
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  • Summary
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a textile defect detection model and its training method and application, thereby solving the problems of low accuracy, poor real-time performance and no general technical issues

Method used

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  • A textile defect detection model and its training method and application
  • A textile defect detection model and its training method and application
  • A textile defect detection model and its training method and application

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Embodiment 1

[0062] The convolutional neural network used in the present invention can accept the input of any pixel image in principle, but considering that an image with too large pixels will cause small defects to be distorted after resize, and an image with too small pixels will cause some features to be extracted. , the reference pixel given by the official is 416×416. In embodiment 1 of the present invention, a pixel size of 1216×1020 is adopted in consideration of the large scope of one-time identification of cloth and the adequacy of feature extraction.

[0063] An application of a textile defect detection model, including:

[0064](1) Use an industrial camera to collect and select images containing six types of defects including warp breaks, weft breaks, holes, foreign matter, oil stains, and creases as sample textile defect images. The sample textile defect images are in jpg format with a resolution of 96dpi The three-channel color image with a pixel size of 1216×1020; create two...

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Abstract

The invention discloses a textile defect detection model and its training method and application, wherein the training method includes collecting sample textile defect images, establishing a data set, and establishing a textile defect detection model based on YOLOv2; using dimension clustering before training the model , when training the model, perform direct coordinate prediction, loss value calculation, and backpropagation to obtain the current network weight parameters; use the current network weight parameters to update the network weight parameters of the textile defect detection model, and then use the training set to perform multiple network weight parameters Calculate and update to obtain the optimal network weight parameters, that is, to obtain the trained textile defect detection model. Then the textile image is collected in real time, and the trained textile defect detection model is used for detection, and the defect detection result of the textile image is obtained. Using the textile fabric defect detection model of the invention has high defect accuracy, strong real-time performance and universality.

Description

technical field [0001] The invention belongs to the technical field of deep learning and computer vision, and more specifically relates to a textile defect detection model and its training method and application. Background technique [0002] In the production and development of the world's textile industry, the quality inspection of textile fabrics has always been a very important link. However, in the traditional quality inspection of textile fabrics, due to the lack of good automatic inspection tools, most of the solutions are still judged by artificial vision. Work is prone to fatigue, and accuracy is difficult to guarantee. With the rapid increase in the production volume and production speed of textile fabrics, the artificial vision method is increasingly unsuitable for the needs of the modern textile industry. It is urgent to find an automatic, accurate and fast method for quality or defect detection. At present, the detection methods for textile defects in China in...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G01N21/88G06N3/04G06N3/08
CPCG06N3/084G01N21/88G01N2201/1296G06F30/00G06N3/045
Inventor 孙志刚禹万泓江湧王卓肖力
Owner HUAZHONG UNIV OF SCI & TECH
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