A defect detection method based on local contrast saliency region detection

Abstract

The invention discloses a defect detection method based on local contrast saliency region detection, which comprises the following steps: acquiring a panel image, and dividing the image into a plurality of regions by using a graph-based segmentation method; Establishing a color histogram for each region; Calculating the color distance metrics of the two areas rk and ri; For each region rk, calculating the saliency value of the region rk by measuring the color contrast of the region rk and other regions of the image, namely, calculating the weighted color distance measurement sum of the regionrk, and obtaining a sparse histogram; introducing Spatial information i to increase the influence effect of the area, and defining the significance based on the contrast of the spatial weighted area as the spatial distance between negative distance power weighted areas of an exponential function; setting a saliency threshold value, and if the saliency threshold value is larger than the threshold value, the human eyes are visually sensitive to the saliency threshold value and are very likely slits; if the saliency threshold value is less than the threshold, the region does not have significanceand is impossible to be a slit region.

Description

technical field [0001] The invention relates to the technical field of defect detection, in particular to a defect detection method based on local contrast significant region detection. Background technique [0002] With the continuous improvement of people's living standards and the continuous popularization of electronic equipment, the production and shipment of electronic equipment is very large. How to realize the automatic detection of defects in the production process of electronic equipment products has become one of the concerns of major manufacturers. [0003] Among them, there are many steps in the production process of the glass cover plate of electronic equipment, there are many problems, and there is a lot of room for improvement. The main problems are: [0004] At present, the commonly used printing defect detection methods usually use the edge extraction algorithm based on the glass cover image, and then analyze the contour and the flatness and concave-convex ...

AI Technical Summary

Problems solved by technology

[0004] At present, the commonly used printing defect detection methods usually use the edge extraction algorithm based on the glass cover image, and then analyze the contour and the flatness and concave-convex area of ​​the edge, but the existing technology is still unable to detect subtle defects, because in the subtle Near the defect, the commonly used edge extraction algorithm cannot accurately extract the edge

[0005] In addition, the existing process uses manual visual inspection during the final inspection, which cannot effectively prevent damage to the glass cover caused by machine failure or damage. From the first occurrence of damage to manual detection during the final inspection, a series of The damaged parts cannot be adjusted in time, resulting in loss and waste of the manufacturer

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