基于深度学习的连铸坯表面缺陷检测

doi:10.12066/j.issn.1007-2861.2018

Abstract

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

CLC Number:

TP391.4

Jiacheng HU, Xiangyang WANG, Han LIU. Defect detection of continuous casting slabs based on deep learning[J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(4): 445-452.

Figures/Tables 13

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Defect detection of continuous casting slabs based on deep learning

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