An Effective Method of Weld Defect Detection and Classification Based on Machine Vision

In order to effectively identify and classify weld defects of thin-walled metal canisters, a weld defect detection and classification algorithm based on machine vision is proposed in this paper. With the weld defects categorized, a modified background subtraction method based on Gaussian mixture mod...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2019-12, Vol.15 (12), p.6322-6333
Main Authors: Sun, Jun, Li, Chao, Wu, Xiao-Jun, Palade, Vasile, Fang, Wei
Format: Article
Language:English
Subjects:
Algorithms
Cans
Classification
Classification algorithms
Defects
Design defects
Feature extraction
Gaussian mixture model
Gaussian mixture models
Identification methods
Image classification
Image detection
Machine vision
Metals
Probabilistic models
Subtraction
Vision systems
weld defect classification
weld defect detection
Weld defects
Welding
Welding parameters
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Summary:In order to effectively identify and classify weld defects of thin-walled metal canisters, a weld defect detection and classification algorithm based on machine vision is proposed in this paper. With the weld defects categorized, a modified background subtraction method based on Gaussian mixture models, is proposed to extract the feature areas of the weld defects. Then, we design an algorithm for weld detection and classification according to the extracted features. Next, by using the weld images sampled by the constructed weld defect detection system on a real-world production line, the parameters of the weld defect classifiers are determined empirically. Experimental results show that the proposed methods can identify and classify the weld defects with more than 95% accuracy rate. Moreover, the weld detection results obtained in the actual production line show that the detection and classification accuracy can reach more than 99%, which means that the system enhanced with the proposed method can meet the requirements for the best real-time and continuous weld defect detection systems available nowadays.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2896357