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Volume 9
Issue 16
10.3390/app9163312
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Article

Deep Learning-Based Classification of Weld Surface Defects

by
Haixing Zhu
1,2,
Weimin Ge
1,2 and
Zhenzhong Liu
1,2,3,*
1
Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China
2
National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China
3
State Key Lab of Digital Manufacturing Equipment & Technology, School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3312; https://doi.org/10.3390/app9163312
Submission received: 18 July 2019 / Revised: 5 August 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
(This article belongs to the Special Issue Theory and Application of Computational Intelligence and Deep Learning Paradigms in Robotics and Vision System)
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Abstract

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.
Keywords: weld surface defects; feature extraction; deep learning; convolutional neural network; random forest; image classification weld surface defects; feature extraction; deep learning; convolutional neural network; random forest; image classification

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MDPI and ACS Style

Zhu, H.; Ge, W.; Liu, Z. Deep Learning-Based Classification of Weld Surface Defects. Appl. Sci. 2019, 9, 3312. https://doi.org/10.3390/app9163312

AMA Style

Zhu H, Ge W, Liu Z. Deep Learning-Based Classification of Weld Surface Defects. Applied Sciences. 2019; 9(16):3312. https://doi.org/10.3390/app9163312

Chicago/Turabian Style

Zhu, Haixing, Weimin Ge, and Zhenzhong Liu. 2019. "Deep Learning-Based Classification of Weld Surface Defects" Applied Sciences 9, no. 16: 3312. https://doi.org/10.3390/app9163312

APA Style

Zhu, H., Ge, W., & Liu, Z. (2019). Deep Learning-Based Classification of Weld Surface Defects. Applied Sciences, 9(16), 3312. https://doi.org/10.3390/app9163312

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