Open AccessResearch on image segmentation of inner cylinder wall with annular weld based on deep learning
Author(s) -
Xia Jun Fei,
Feng Qiao Sheng,
Dong Xiang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1486/7/072084
Subject(s) - artificial intelligence , computer science , segmentation , feature (linguistics) , welding , computer vision , deep learning , pixel , convolutional neural network , interference (communication) , image (mathematics) , cylinder , pattern recognition (psychology) , materials science , mathematics , channel (broadcasting) , telecommunications , geometry , composite material , philosophy , linguistics
Based on the characteristics of autonomous learning with deep learning and high recognition rate, an image segmentation study based on Faster R-CNN for the inner wall annular weld is proposed. The method extracts pixels containing annular weld feature information by using an RPN network to improve target detection speed. Then, the convolutional layer output detection model is shared by the RPN network and the Fast R-CNN network to realize accurate detection of the annular weld in the video image. The research results show that the proposed method can accurately detect the circumferential weld bead and segment the image under the condition of poor video image quality, and has the advantages of strong anti-interference ability and accurate identification.