Cloth defect detection method and device based on machine learning

Abstract

The invention discloses a cloth defect detection method based on machine learning. The method particularly comprises the following steps of image segmentation, image enhancement, image denoising, sub image feature extraction, defect point area segmentation, offline cloth learning, online cloth detection, and cloth defect point classification. In the offline cloth learning stage, a BP neural network is used to train standard image characteristic parameters, and a standard value is obtained. In the online cloth detection stage, the BP neural network is used to detect the feature parameters of the sub image. In the cloth defect point classification stage, a depth learning algorithm based on a convolutional neural network is used to classify cloth defects. The cloth defect detection method based on machine learning has a self-learning function and can meet the continuously-developing industry needs. The invention also provides a cloth defect detection device based on machine learning, which comprises an image acquisition unit, an image processing unit, a data communication unit and an action execution unit. The detection device can realize high-efficiency and accurate detection, and workers can be freed from heavy and tasteless physical labor.

Description

technical field [0001] The invention relates to the field of cloth detection, in particular to a method and device for detecting cloth defects based on machine learning. Background technique [0002] Textile industry is the pillar industry of my country's national economy. Textile industry, together with steel, automobile, shipbuilding, petrochemical, light industry, non-ferrous metal, equipment manufacturing, electronic information and logistics industries, is the main industry composition of my country. In the process of textile production, surface defects of cloth are the key factors affecting the quality of cloth. Cloth surface defects directly affect the grading of cloth. The price of second-class products is only 45%-65% of first-class products. Fabric defects seriously affect the economic income of the textile industry. Therefore, cloth defect detection is particularly important in textile quality control. [0003] For a long time, cloth inspection is generally done...

AI Technical Summary

Problems solved by technology

[0004] The Chinese patent with the application number: 201410467132.5 discloses the "machine vision-based automatic detection and recognition device and method for cloth defects", although the device and method overcome the traditional artificial vision detection speed, low precision, high missed detection rate, and high false detection rate defects, but it does not have self-learning performance, with the development of industry, it will be difficult to meet higher industrial needs

Images