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Deep learning-based welding image recognition: A comprehensive review
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AI
DOI：10.1016/j.jmsy.2023.05.026.png)
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摘要
En 中文
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 arti-ficial 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.
Keyword:
Welding system
Computer vision
Image recognition
Deep learning
Convolutional neural network
Explainable AI
期刊
IF:
14.2
论文数:
2.7K
被引数:
1.6W
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