A method for surface defect detection based on Fourier transform and image gradient features

Abstract

The invention belongs to the technical field related to image processing, and discloses a method for realizing surface defect detection based on Fourier transform and image gradient features, comprising the following steps: (1) converting the collected workpiece surface image into a grayscale image; (2) seek the gradient direction feature map of the image, and compress the gradient direction; (3) generate a gradient direction rectangle and a vertical gradient direction rectangle; (4) calculate the gradient direction rectangle and the vertical gradient direction rectangle respectively Grayscale features, and write them into a new image to obtain the corresponding gradient direction feature map and vertical gradient direction feature map; (5) Multiply the gradient direction feature map and vertical gradient direction feature map, and do grayscale stretching Transformation; (6) Threshold extraction is performed on the image to obtain the suspected defect area, and the contrast screening is performed on the suspected defect area to obtain the target area, and then the surface defect detection is completed. The invention improves accuracy and real-time performance.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more particularly, relates to a method for realizing surface defect detection based on Fourier transform and image gradient features. Background technique [0002] During the machining process, the workpiece usually leaves periodic textures due to turning, and affected by tool wear and foreign matter in the grinding tool, defect areas of different shapes will be left on the outer surface of the workpiece. Under the combined action of unstable optical elements and different surface morphologies of workpieces, the contrast of defects on the surface of workpieces is greatly different. Therefore, how to efficiently detect defect areas with different contrasts on the surface of workpieces with periodic textures has become an urgent problem to be solved in industrial production lines. [0003] Among the defect detection methods based on machine vision, threshold extraction of defect areas...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method for detecting surface defects based on Fourier transform and image gradient features. For the requirement of accurate and real-time inspection, the frequency domain image is generated by Fourier transform, and the texture feature of the image is removed in the frequency domain. For the image after texture removal, the difference between the gray level of the defect area and the gray level of the surrounding area is very weak. The gradient feature of the image is the characterization of the largest grayscale change in the image, and the gradient direction is also the direction of the largest grayscale change. Using this feature to generate a gradient direction rectangle and a vertical gradient direction rectangle, the gray difference between the defect area and the non-defect area is enlarged. Enhance the defect area features to realize the detection of different contrast defect areas

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