Fabric surface defect detection method based on convolutional neural network

Abstract

The invention discloses a fabric surface defect detection method based on a convolutional neural network. The fabric surface defect detection method comprises the following steps: S1, collecting and marking a data set; S2, manufacturing GroundTruth of a defect sample image of the data set; S3, constructing a convolutional neural network model; S4, training a convolutional neural network model to obtain an optimal model; and S5, acquiring the defect image of the fabric online, inputting the image of the fabric to be detected into the trained convolutional neural network model for image segmentation, and realizing online automatic detection through the convolutional neural network model so as to identify defects existing on the surface of the fabric. According to the method, the defects of artificial design defect features can be overcome, the features can be learned from the pre-marked sample data set by using the convolutional neural network, so that the segmentation is quickly and accurately performed, the fabric surface defects are accurately and automatically detected, the manpower and material resources are saved, and the fabric product quality is improved.

Description

technical field [0001] The invention relates to the technical field of fabric surface defect detection, in particular to a fabric surface defect detection method based on a convolutional neural network. Background technique [0002] With the rapid development of the textile industry, people's control over the quality of fabrics is becoming more and more stringent. In the textile industry, various unfavorable factors such as human error, machine failure, and yarn breakage can easily cause fabric defects and affect product quality. It will cause huge economic losses to the enterprise, so fabric defect detection is one of the important links of quality control. [0003] There are various types and shapes of defects. The traditional detection process mainly relies on human eyes to identify them. At present, most textile enterprises rely on artificial vision to detect fabric defects. Due to the influence of objective factors, the precision and accuracy of detection cannot be gua...

AI Technical Summary

Problems solved by technology

[0012] The purpose of the present invention is to overcome the fact that human eye recognition in fabric defect detection in the prior art is easily affected by subjective and objective factors such as personal vision, fatigue, emotion, and illumination. For technical problems that are difficult to meet the requirements, a fabric surface defect detection method based on convolutional neural network is provided to realize rapid and accurate detection of fabric surface defects, save manpower and material resources, improve production efficiency, and ensure the quality of fabric products

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