A Method for Surface Defect Identification of Compressor Cast Iron Parts Based on Multi-Scale Texture Analysis

Abstract

This invention belongs to the field of image processing technology and relates to a method for identifying surface defects in compressor cast iron parts based on multi-scale texture analysis. The method involves sliding a detection window on a grayscale image of the cast iron part surface to be inspected, constructing a gradient histogram, and calculating the concentration of tool marks to determine the window category. Multi-scale binary texture encoding is performed using the principal gradient direction as the starting angle. The normalized texture entropy value at each scale is calculated as the entropy anomaly, and the principal inspection scale and principal entropy anomaly are selected based on the window category. The proportion of uniform patterns after encoding normalization at each scale is calculated to obtain the uniform anomaly and the principal uniform anomaly. Finally, in response to the principal entropy anomaly and the principal uniform anomaly satisfying a dynamic first preset threshold and a second preset threshold, respectively, the defect identification result is output. This invention effectively avoids the dilution of defect signals by background features and improves the sensitivity and noise resistance of detecting small defects in complex, multi-modal backgrounds.

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