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Journal of Shanghai University >

2019 , Vol. 25 >Issue 4: 445 - 452

DOI: https://doi.org/10.12066/j.issn.1007-2861.2018

Research Articles

Defect detection of continuous casting slabs based on deep learning

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  • 1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
    2. Shanghai Jinyi Inspection Technology Co., Ltd., Shanghai 201900, China

Received date: 2017-12-18

  Online published: 2019-09-04

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Abstract

For analyzing surface defect such as surface cracks, depressions, inclusions, blow hole and scratches of continuous casting slabs, the support vector machine (SVM) can be used, and this is realized through identifying with the image texture and geometric features. The accuracy with such method is about 83${\%}$. Convolutional neural network (CNN) is used to propose a method of defect detection. The crack defect detection accuracy is 93${\%}$, and the detection accuracy for other defects (like depression, inclusions, blow hole and scratches when they are grouped together) is 88${\%}$. Experimental results show that deep learning is an effective method for detecting and identifying the defective continuous casting slabs, and the accuracy is high.

Key words： defect detection; support vector machine (SVM); deep learning

Cite this article

Jiacheng HU, Xiangyang WANG, Han LIU . Defect detection of continuous casting slabs based on deep learning[J]. Journal of Shanghai University, 2019 , 25(4) : 445 -452 . DOI: 10.12066/j.issn.1007-2861.2018

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