Surface Defect Detection and Root Cause Analysis

Tianchen Liu; Fan Zhu; Haoran Yu; Haisong Gu

Surface Defect Detection and Root Cause Analysis

Tianchen Liu *

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Fan Zhu

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Haoran Yu

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Haisong Gu

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Surface Defect Detection and Root Cause Analysis

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Abstract

Artificial Intelligence has played an increasingly important role in surface defect detection in recent years. At the same time, there are many challenges using deep learning for this area, such as the detection accuracy, shortage of data and, lack of knowledge of root cause of defects. To solve the problem of data shortage, we propose a taxonomy method called Dataonomy TM to extend a meta defect datasets with a small number of samples for training defect classifiers. For the accuracy, we apply two latest deep neural network(DNN) architectures, Inception v3 and fully convolutional networks (FCN) so as not only to classify whether there are defects but also to make a pixel-wise prediction to inference the areas of defects. For those detected defects, we combine DNN with traditional AI methods to find root causes of detected defects. We use a generalized multi-image matting algorithm to extract common defects automatically. We apply this technology to identify defects that stem from systematic errors in the surface operation. Experimental results have shown great capability and versatility of our proposed methods.

References

9 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Tianchen Liu, Fan Zhu, Haoran Yu, Haisong Gu. 2020. "Surface Defect Detection and Root Cause Analysis". Global Journal of Science Frontier Research, Global Journal of Science Frontier Research - I: Interdisciplinary GJSFR-I Volume 20 (GJSFR Volume 20 Issue I3).

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GJSFR Volume 20 Issue I3

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

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Classification

GJSFR-I Classification FOR Code: 080199

Submission ReceivedMay 27, 2020
Peer Review Double Blind
Handling Editor
Accepted June 22, 2020
Published July 5, 2020

Version of record

v1.2

Issue date

August 11, 2020

Language

English

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