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<p>Smart Digital Twins (<i>SDTs</i>) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation, enabling the optimization of the performance of these systems by controlling the actions of systems. Recently, the Deep Learning (<i>DL</i>) models have significantly enhanced the capabilities of <i>SDTs</i>, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, <i>SDTs</i> use image data (image-based <i>SDTs</i>) to observe, learn, and control system behaviors. This paper focuses on various approaches and associated challenges in developing image-based <i>SDTs</i> by continually assimilating image data from physical systems. The paper also discusses the challenges in designing and implementing <i>DL</i> models for <i>SDTs</i>, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based <i>DL</i> approaches to develop robust <i>SDTs</i> are provided. This includes the potential for using generative models for data augmentation, developing multi-modal <i>DL</i> models, and exploring the integration of <i>DL</i> models with other technologies, including Fifth Generation (<i>5&#xa0;G</i>), edge computing, and the Internet of Things (<i>IoT</i>). In this paper, we describe the image-based <i>SDTs</i>, which enable broader adoption of the Digital Twins (<i>DTs</i>) paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of <i>SDTs</i> in replicating, predicting, and optimizing the behavior of complex systems.</p>

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Image-based deep learning for smart digital twins: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月24日

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  • Md Ruman Islam,
  • Mahadevan Subramaniam,
  • Pei-Chi Huang

摘要

Smart Digital Twins (SDTs) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation, enabling the optimization of the performance of these systems by controlling the actions of systems. Recently, the Deep Learning (DL) models have significantly enhanced the capabilities of SDTs, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, SDTs use image data (image-based SDTs) to observe, learn, and control system behaviors. This paper focuses on various approaches and associated challenges in developing image-based SDTs by continually assimilating image data from physical systems. The paper also discusses the challenges in designing and implementing DL models for SDTs, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based DL approaches to develop robust SDTs are provided. This includes the potential for using generative models for data augmentation, developing multi-modal DL models, and exploring the integration of DL models with other technologies, including Fifth Generation (5 G), edge computing, and the Internet of Things (IoT). In this paper, we describe the image-based SDTs, which enable broader adoption of the Digital Twins (DTs) paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of SDTs in replicating, predicting, and optimizing the behavior of complex systems.