Method for detecting and classifying defects of non-woven fabrics

Abstract

The invention discloses a method for detecting and classifying defects of non-woven fabrics, and the problems of the automatic detection and classification of four defects including holes, oil stains,foreign objects and scratches of the non-woven fabrics are solved. The method comprises a step of detecting a non-woven fabric defect image, filtering the image by an optimized Gabor filter group, fusing a filtering result, binarizing the result by using an adaptive threshold segmentation method, eliminating noise interference by a pseudo-defect culling algorithm, and thus accurately determiningthe positions of the defects in the image, a step of segmenting a region of interest in the image according to the position of the defects, and extracting a composite feature vector formed by a shapefeature, a first-order moment feature and a second-order moment feature based on the region of interest, a step of training an SVM classifier by using a composite feature vector group and a one-to-onedesign strategy, and a step of finally accurately classifying the defect characteristics of the non-woven fabrics by using the trained classifier group. The method has the advantages of the accuratepositioning of the defects and high accuracy of classification and is used for detecting and classifying cloth defects of non-woven fabric manufacturers.

Description

technical field [0001] The invention belongs to the field of image recognition, and in particular relates to a non-woven fabric defect detection and classification method, which can be used to identify defect images collected in a non-woven fabric quality inspection link. Background technique [0002] As an effective quality assurance method, non-woven defect detection is currently mainly implemented manually, with a large workload and low detection efficiency. Therefore, the use of automated machine vision inspection is a reasonable choice, which can ensure a high detection speed and detection rate. For the detection and positioning of non-woven defects, there are mainly methods based on Gabor filtering. Liu Haiping from Huazhong University of Science and Technology uses multi-directional and multi-scale Gabor filters to detect and locate non-woven fabric defects. The detection accuracy is high, but the real-time performance is poor. Zhang Bo et al. of Huazhong University...

AI Technical Summary

Problems solved by technology

Huazhong University of Science and Technology Zhang Bo and others used a three-layer BP neural network to automatically classify non-woven fabric defects, and the classification accuracy can reach 87.05%. However, the neural network algorithm requires a large number of training samples, and it is difficult to implement

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