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Weighted Matrix Decomposition for Small Surface Defect Detection.

Zhiyan Zhong¹, Hongxin Wang^2,3, Dan Xiang^1,4

¹School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China.

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces a weighted matrix decomposition (WMD) model for accurately detecting small defects on complex surfaces. The WMD model enhances contrast and effectively segments defects, outperforming existing methods for improved industrial quality control.

Keywords:

defect detection machine vision weighted matrix decomposition

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Area of Science:

Computer Vision
Image Processing
Materials Science

Background:

Detecting small defects on complex surfaces is critical for industrial quality control.
Existing methods struggle with localizing and segmenting small defects due to weak feature representation.

Purpose of the Study:

To develop an effective model for small defect detection on complex surfaces.
To address the limitations of current methods in handling defects with weak features.

Main Methods:

A weighted matrix decomposition (WMD) model was proposed.
A weighted matrix was constructed using RGB channel texture characteristics to enhance defect-background contrast.
The weighted matrix was decomposed into low-rank (background) and sparse (defect) matrices.
An automatic threshold segmentation method was employed for defect area and edge segmentation.

Main Results:

The proposed WMD model significantly improved the detection of small defects.
The model demonstrated superior performance compared to state-of-the-art methods.
Quantitative evaluation metrics confirmed the effectiveness of the WMD model.

Conclusions:

The WMD model offers a robust solution for small defect detection on complex surfaces.
The method shows promising industrial application prospects for quality assurance.