An Effective Framework of Automated Visual Surface Defect Detection for Metal Parts

Developing effective automated visual surface defect detection of metal parts is a challenge due to highly reflective surfaces and miscellaneous defect patterns. In this work, an effective framework consisting of an image registration module and a defect detection module is proposed to cope with thi...

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Bibliographic Details
Published in:IEEE sensors journal 2021-09, Vol.21 (18), p.20412-20420
Main Authors: Qiu, Kepeng, Tian, Luo, Wang, Peng
Format: Article
Language:English
Subjects:
Algorithms
Automation
Datasets
Defect detection
Feature extraction
image difference
Image registration
machine vision
Magnetic resonance imaging
metal parts
Metals
Modules
Principal components analysis
Registration
Stainless steels
Surface defects
Surface morphology
Surface texture
Surface treatment
Visualization
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Summary:Developing effective automated visual surface defect detection of metal parts is a challenge due to highly reflective surfaces and miscellaneous defect patterns. In this work, an effective framework consisting of an image registration module and a defect detection module is proposed to cope with this challenge. First, an algorithm based on dual weighted principal component analysis is designed for the image registration module. In this algorithm, the visual feature and spatial feature are integrated into a unified principal component to obtain highly accurate image registration results. Then, a defect detection module is constructed using an image difference algorithm with prior constraints. In this module, two categories of constraint bounding boxes are designed to reduce the misdetection due to the susceptible edges and improve the accuracy of surface defect detection of metal parts. Three applications, including a magnetic resonance imaging dataset, a printed circuit board dataset, and an industrial case of the stainless-steel positioning pins are provided to illustrate the superiority of the proposed framework.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3095410