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Open AccessArticle

Deep Learning-Based Classification of Weld Surface Defects

1,2, 1,2 and 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
Received: 18 July 2019 / Revised: 5 August 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
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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. View Full-Text
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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Zhu, H.; Ge, W.; Liu, Z. Deep Learning-Based Classification of Weld Surface Defects. Appl. Sci. 2019, 9, 3312.

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