Method for detecting defect area of color texture fabric based on self-attention

Abstract

The invention discloses a method for detecting a defect area of a color texture fabric based on self-attention, and the method specifically comprises the steps: building a color texture fabric data set comprising color texture defect-free images, and carrying out the superposition of noise of the color texture defect-free images in the color texture fabric data set; constructing a Swin-Unet model based on Transformer, and obtaining a trained model through training; and reconstructing a to-be-detected color texture fabric image by using the trained model, outputting a corresponding reconstructed image, and then judging and positioning a defect area according to the reconstructed image. According to the method for detecting the defect area of the color texture fabric based on self-attention, the problems that in the prior art, due to deepened layers of a convolutional neural network, overfitting of a model is often caused, and then missing detection or over-detection of the defect area is caused, therefore, the problem of detecting the defect area of the colored texture fabric cannot be effectively solved.

Description

technical field [0001] The invention belongs to the technical field of defect detection methods, and relates to a self-attention-based detection method for color texture fabric defect regions. Background technique [0002] Colorful textured fabrics have beautiful and diverse patterns, and their sales have increased rapidly in recent years. They are not only used in clothing manufacturing, but also in industrial products. However, in its production process, due to irresistible factors, there will be defects on the surface of the fabric. At present, most enterprises use manual visual inspection to detect defects, but manual visual inspection will be affected by the degree of human eye fatigue, resulting in low efficiency and high missed detection rate. Therefore, there is a need for an accurate and fast automatic defect detection method for colored textured fabrics. [0003] At present, the fabric defect detection technology based on machine vision has received extensive att...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a self-attention-based detection method for color texture fabric defect areas, which solves the problem in the prior art that the over-fitting of the model is often caused by the deepening of the convolutional neural network level, which in turn leads to defect areas. Missing or over-inspection, which cannot effectively solve the problem of detection of color texture fabric defect areas

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