Surface defect detection method of robust based on machine vision

Abstract

The invention relates to a surface defect detection method of robust based on machine vision. The method comprises the following steps: 1) performing gray-scale treatment on the input colorful images to obtain a grey-scale image; 2) when the damage gray value in the grey-scale image is higher than that of the background, inverting the color of the gray-scale image; 3) based on the provided binarizition threshold optimization function, calculating the optimal threshold value; 4) based on the optimal threshold value, performing binaryzation on the image; and 5) performing contour detection on the image after binarizition. According to the provided method for calculating the optimal threshold value by the binarizition threshold optimization function, compared with the traditional binaryzation operation such as an iterative threshold method, an otsu method, a bimodal average method and an one-dimensional maximum entropy method, the method has strong robustness, damage position can be accurately extracted from the image containing the surface defect, and the proportion of the non damaged image with false detection as damaged is greatly reduced. The parameter can be conveniently adjusted, and the realization process is simple.

Description

[0001] technology neighborhood [0002] The invention belongs to the field of image processing and relates to a robust machine vision-based surface defect detection method. Background technique [0003] At present, due to the popularization of machine vision, surface defect detection methods based on machine vision are very extensive. In the field of computer vision and image processing, image binarization processing method is one of the most basic and important research contents of image analysis and recognition. , the general surface defect detection method based on machine vision is to detect the image through binarization. At present, there are many image binarization methods, but the detection effect is often poor. Lack of robustness, often only for one detection object surface, such as fabric, metal and glass and so on. Conventional binarization algorithms often have a high false detection rate, and damages are often detected incorrectly in images that do not contain d...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the existing binarization detection algorithm can only target one detection object surface, such as fabric, metal and glass, etc.

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