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Surface defect detection method for glass substrate using improved Otsu segmentation.

Zhiyong He, Lining Sun

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Summary

This summary is machine-generated.

This study introduces an improved Otsu thresholding segmentation method for detecting surface defects in glass substrates. The novel approach enhances noise resistance and computational efficiency in automated optical inspection.

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Area of Science:

Materials Science
Computer Vision
Image Processing

Background:

Image quality degradation due to noise complicates automated optical inspection (AOI) of surface defects.
Traditional Otsu segmentation methods exhibit insufficient anti-noise capabilities for defect detection.

Purpose of the Study:

To develop an improved thresholding segmentation method for detecting surface defects in glass substrates.
To enhance the anti-noise ability and efficiency of traditional Otsu segmentation for AOI applications.

Main Methods:

A novel method based on thresholding segmentation is proposed.
A straight-line intercept histogram is established from 2D image information.
Otsu criteria are applied to a 1D histogram for optimal threshold determination.
Contrast features are extracted post-segmentation for defect identification.

Main Results:

The improved Otsu algorithm demonstrates robust anti-noise performance.
The method is simpler and more efficient than traditional 2D Otsu methods.
Experimental results confirm the effectiveness of the proposed approach for surface defect detection.

Conclusions:

The developed method effectively detects surface defects in glass substrates.
The improved Otsu algorithm offers a computationally efficient and noise-robust solution for AOI.
This technique enhances the reliability of automated inspection systems.