Feature extraction and defect classification method of magnetic tile surface defects based on machine vision

Abstract

The invention provides a magnetic tile surface defect feature extraction and defect classification method based on machine vision. A concrete algorithm comprises a first step of building a 5-scale and 8-direction Gabor filter bank suitable for magnetic tile surface defect feature extraction, conducting filtering to an original image and obtaining a 40-width component plot, a second step of respectively extracting a gray average and a variance feature of the component plot and forming a 80-dimension feature vector, a third step of conducting dimensionality reduction to the original 80-dimension feature vector through a principal component analysis (PCA) method and an independent component analysis (ICA) method, removing relevance and redundancy and obtaining a 20-dimension feature vector, a fourth step of conducting normalization pretreatment to feature vector data, wherein the original data are normalized between zero and one, and a fifth step of adopting a grid method and a K-CV method to achieve SVM parameter optimization at first and training an SVM model using training sample data offline, wherein pretreated testing sample data are input into a support vector machine during online testing, and automatic classification and identification of defects can be achieved. The feature extraction method can effectively filter interference and prominent defects of magnetic tile surface texture, extracted features can reflect defect information accurately, data values are small, and a classifier used for classifying the defects can achieve defect identification fast and accurately online.

Description

technical field [0001] The invention relates to the field of machine vision detection, in particular to a method for detecting and classifying surface defects of magnetic tiles through machine vision technology. Background technique [0002] Magnetic tile is one of the main products of ferrite permanent magnet materials. It is used in many fields and is an important component in electric motors. It cannot be replaced in recent decades, and the market demand has been very strong. Due to the characteristics of the material and manufacturing process of the magnetic tile itself, it is easy to damage the surface and cause defects, and the magnetic tile is often damaged during transportation. Common defects include cracks, collapses, unqualified chamfers, under-grinding, and grades. , stains, etc. Such defective magnetic tiles should be removed before leaving the factory to prevent sudden damage after being installed in the motor, resulting in bad consequences. The detection of ...

AI Technical Summary

Problems solved by technology

[0005] The invention provides a feature extraction and defect classification method of magnetic tile surface defects based on machine vision, realizes automatic detection of magnetic tile surface defects, and solves the problems of large workload, low efficiency, and high missed detection rate of manual visual inspection

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