TY - JOUR
T1 - Deep learning-based welding image recognition: A comprehensive review
AU - Liu, Tianyuan
AU - Zheng, Pai
AU - Bao, Jinsong
N1 - Funding Information:
This research is partially funded by the Mainland-Hong Kong Joint Funding Scheme ( MHX/001/20 ), Innovation and Technology Commission (ITC), Hong Kong Special Administration Region of the People's Republic of China , National Key R&D Programs of Cooperation on Science and Technology Innovation with Hong Kong, Macao and Taiwan ( SQ2020YFE020182 ), Ministry of Science and Technology (MOST) of the People's Republic of China, and the Centrally Funded Postdoctoral Fellowship Scheme (1-YXBM), The Hong Kong Polytechnic University .
Publisher Copyright:
© 2023 The Society of Manufacturing Engineers
PY - 2023/6
Y1 - 2023/6
N2 - The reliability and accuracy of welding image recognition (WIR) is critical, which can largely improve domain experts’ insight of the welding system. To ensure its performance, deep learning (DL), as the cutting-edge artificial intelligence technique, has been prevailingly studied and adopted to empower intelligent WIR in various industry implementations. However, to date, there still lacks a comprehensive review of the DL-based WIR (DLBWIR) in literature. Aiming to address this issue, and to better understand its development and application, this paper undertakes a state-of-the-art survey of the existing DLBWIR research holistically, including the key technologies, the main applications and tasks, and the public datasets. Moreover, possible research directions are also highlighted at last, to offer insightful knowledge to both academics and industrial practitioners in their research and development work in WIR.
AB - The reliability and accuracy of welding image recognition (WIR) is critical, which can largely improve domain experts’ insight of the welding system. To ensure its performance, deep learning (DL), as the cutting-edge artificial intelligence technique, has been prevailingly studied and adopted to empower intelligent WIR in various industry implementations. However, to date, there still lacks a comprehensive review of the DL-based WIR (DLBWIR) in literature. Aiming to address this issue, and to better understand its development and application, this paper undertakes a state-of-the-art survey of the existing DLBWIR research holistically, including the key technologies, the main applications and tasks, and the public datasets. Moreover, possible research directions are also highlighted at last, to offer insightful knowledge to both academics and industrial practitioners in their research and development work in WIR.
KW - Computer vision
KW - Convolutional neural network
KW - Deep learning
KW - Explainable AI
KW - Image recognition
KW - Welding system
UR - http://www.scopus.com/inward/record.url?scp=85163286293&partnerID=8YFLogxK
U2 - 10.1016/j.jmsy.2023.05.026
DO - 10.1016/j.jmsy.2023.05.026
M3 - Review article
AN - SCOPUS:85163286293
SN - 0278-6125
VL - 68
SP - 601
EP - 625
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
ER -