Fabric defect detection method based on deep learning algorithm

Abstract

The invention relates to a fabric defect detection method based on a deep learning algorithm, and the method comprises the following steps: carrying out the noise filtering and noise enhancement of a fabric original image through employing the image preprocessing technology; carrying out the decomposition and reconstruction of the fabric image after preprocessing through a Daubechies wavelet transformation method, carrying out the feature extraction of images in horizontal, vertical and diagonal directions after reconstruction, and forming a feature vector of the images; Carrying out the analysis and processing of the extracted feature vector of the fabric image through employing a multi-hidden-layer BP neural network model, and achieving the recognition and classification of defects in the fabric image. The method can automatically recognize the defects, and classifies the recognized defects.

Description

technical field [0001] The invention relates to the technical field of fabric defect detection, in particular to a fabric defect detection method based on a deep learning algorithm. Background technique [0002] my country is a big textile country, and the textile industry occupies an important position in the social economy. The quality of the fabric is the key, and the detection of fabric defects is the most important. At present, most production lines still use manual defect detection, which is slow and inefficient, and is easily missed or wrongly detected by the subjective influence of inspectors. With the continuous development of machine vision, image processing technology and machine learning algorithms are gradually applied to the textile industry. The development of these technologies makes defect detection automatic, so as to achieve the purpose of fast and efficient. [0003] Nowadays, scholars at home and abroad have published a large number of related articles ...

AI Technical Summary

Problems solved by technology

[0003] Nowadays, scholars at home and abroad have published a large number of related articles and research results, and new methods have been emerging, which have steadily improved the level of scientific research. However, there are few relatively mature automatic detection systems, and their ability to distinguish defects is limited. However, more advanced Automatic detection systems are rare after all

The fabric automatic detection system developed by some foreign companies mainly has some defects, such as their limited ability to distinguish the type of fabric defects, high cost of practical application, large limitations, etc.

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