Gabor filtering and deep neural network-based warp-knitted jacquard fabric defect detection method

Abstract

The invention discloses a Gabor filtering and deep neural network-based warp-knitted jacquard fabric defect detection method. The method comprises two parts including a training stage and a detectionstage. In the model training stage, a defect-free fabric image is adopted; a training sample set is obtained through image preprocessing; and then parameters in a deep neural network are trained. During detection, a detection sample set of a to-be-detected image is obtained; then a network output is obtained by adopting the deep neural network; and finally a defect detection result is obtained. AGabor filter is adopted for extracting texture features of a fabric; a cost function value is calculated according to a Fisher criterion; textures of all directions and sizes in the image can be extracted; and the detection accuracy is improved. Image features are automatically selected by adopting the deep neural network, so that the defect and deficiency of artificial feature selection in an existing robot vision-based defect detection method are overcome.

Description

Technical field: [0001] The invention relates to the technical field of textile product detection, in particular to a warp-knitted jacquard defect detection method based on Gabor filtering and a deep neural network. Background technique: [0002] In the process of fabric production, fabric defects are inevitable. The traditional manual detection method has problems such as the subjective influence of human beings on the detection results, high missed detection rate, and high labor cost, which has increasingly become the bottleneck of enterprise development. With the development of computer technology, the method of automatic detection of fabric defects relying on machine vision has the advantages of high stability, saving labor costs, and improving production efficiency, and has gradually been developed and applied. [0003] At present, the fabric defect detection method based on machine vision is more effective for the detection of warp-knitted plain fabrics, but the detec...

AI Technical Summary

Problems solved by technology

[0005] The object of the present invention is to provide a kind of warp knitting jacquard fabric defect detection method based on Gabor filter and deep neural network, utilize the characteristics of deep learning to automatically select features, apply deep learning technology to the field of fabric defect detection, solve the existing defect detection method Drawbacks and deficiencies of artificially selected features

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