Band steel surface defect feature extraction and classification method

Abstract

The invention discloses a band steel surface defect feature extraction and classification method, and belongs to the fields of mode recognition and image processing. The band steel surface defect feature extraction and classification method comprises the steps: extracting a reference sampling size chart of a band steel surface defect sample database; obtaining a reference sampling image, and constructing a gradient size and direction co-occurrence matrix; by aiming at a defect inner area of the reference sampling image, constructing a grayscale size and direction co-occurrence matrix; generating a feature vector sample training library; trimming a training sample set and extracting a multiplying factor by a method of combining K-nearest neighbour with R-nearest neighbour; improving a classifier by using a multiplying factor of the trimmed sample; obtaining a multi-class classifier model; according to the reference sampling size chart, converting the defect test sample into a reference sampling image, then extracting a 25-dimensional feature quantity, inputting the 25-dimensional feature quantity into the multi-class classifier model, and finishing the defect automatic recognition. According to the band steel surface defect feature extraction and classification method, the scale and rotation are not changed, the influence by other adverse factors is restrained, and recognition efficiency and accuracy are improved.

Description

technical field [0001] The invention belongs to the field of pattern recognition and image processing, and in particular relates to a method for defect feature extraction and defect classification in the direction of defect recognition on the surface of strip steel. Background technique [0002] In recent years, with the ever-increasing demand for high-quality strip steel products and increasingly fierce market competition, the detection of surface defects has become an important technical support for iron and steel enterprises to implement strip quality monitoring and control. Various types of defects will appear during the production and processing of strip steel, such as: cracks, scars, holes, scale, curling, scratches, etc. An important process of surface defect monitoring is to identify and distinguish these defects in order to quickly deal with and control product quality problems. The process of strip surface defect identification mainly includes four links: defect p...

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Problems solved by technology

However, in the actual classification problem, there is a conflict between the classification accuracy and efficiency of the traditional support vector machine

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