Integration of Computer Vision and Digital Twin Technologies for Intelligent Industrial Monitoring: A Comprehensive Review
Keywords:
Computer vision, Digital twin, Edge-based inference, Multi-modal sensor fusion, Vision-integrated digital twinAbstract
Digital twin technology and computer vision have independently advanced as pillars of intelligent cyber-physical systems, and the convergence of the two fields is increasingly regarded as a pathway toward richer, more autonomous, and more accurate virtual representations of physical assets. This review examines the theoretical foundations, technical enablers, and application domains associated with vision-enabled digital twins. A layered conceptual framework is proposed, structured around sensing, visual perception, real-time synchronization, simulation, and decision-support components. The role of deep learning architectures, including convolutional neural networks, transformer-based detectors, and 3D reconstruction methods, is discussed in relation to their contribution toward automated state estimation, geometry capture, and anomaly detection within digital twin environments. Reported implementations across manufacturing, healthcare, smart infrastructure, and autonomous transportation are synthesized to illustrate performance trends, integration strategies, and reported benefits such as reduced downtime, improved predictive maintenance accuracy, and enhanced situational awareness. Persistent challenges are identified, including latency in bidirectional data synchronization, limited generalization of vision models across varying environmental conditions, high computational demand, and vulnerability to cybersecurity threats. Future research directions are outlined, emphasizing edge-based inference, multimodal sensor fusion, and standardized interoperability protocols. The review is intended to serve as a consolidated reference for researchers and practitioners pursuing vision-integrated digital twin solutions across industrial and urban contexts.
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