Deep Learning-Based Classification of Weld Surface Defects

Applied Sciences

Haixing Zhu¹  Zhenzhong Liu¹  Weimin Ge¹ 

[1] Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China;

关键词: weld surface defects;    feature extraction;    deep learning;    convolutional neural network;    random forest;    image classification;   

DOI  :  10.3390/app9163312

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown