Method for reducing dimensions of texture features for surface defect detection on basis of machine vision

Abstract

The invention discloses a method for reducing dimensions of texture features for surface defect detection on the basis of machine vision. The method has the advantages that noise samples and safety samples among training samples are removed, boundary samples replace randomly selected samples to be used as sample sets used during feature weight iteration, different sample weights are attached to three most adjacent samples according to difference in importance degrees of the three most adjacent samples when the feature weights are computed, accordingly, the pertinence of selection on features with high category correlations is improved, the interference degree of noise is reduced, and the method is high in adaptability; correlation coefficient matrixes are acquired, adaptive threshold values are set, redundant features are removed, the features with the high category correlations are extracted, the dimensions of the features are reduced while the classification and identification accuracy is guaranteed, online feature extraction, classification and identification speeds can be greatly increased, and problems of long online feature extraction time and decrease of prediction accuracy due to the fact that high-dimensional features possibly contain redundant features and even noise features can be solved.

Description

technical field [0001] The invention relates to an industrial image recognition and classification method based on machine vision, specifically a method for dimensionality reduction of texture features of industrial product images, and belongs to the technical field of machine vision automatic detection of surface blemishes. Background technique [0002] Compared with artificial vision, the automatic inspection technology based on machine vision has the advantages of fast speed, high precision and never fatigue. In the industrial production line, it is gradually replacing artificial visual inspection, which not only reduces labor costs but also realizes the improvement of product quality strict control. [0003] Feature selection is one of the key steps in workpiece defect recognition and classification, and the quality of the selected features largely affects the final recognition and classification results. In order to describe the sample more comprehensively, multi-dimen...

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Problems solved by technology

[0005] In order to solve the problems that high-dimensional features may contain redundant features or even noise features, which lead to long online feature extraction time and reduced prediction accuracy, the present invention provides an offline feature dimensionality reduction suitable for surface defect detection of textured workpieces method

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