[Submitted on 30 Jun 2026]
Title:A Synthetic-Driven Vision System for Assembly Step Recognition
View a PDF of the paper titled A Synthetic-Driven Vision System for Assembly Step Recognition, by Hui Zhang and 4 other authors
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Abstract:Quality control in industrial assembly is essential, and real-time monitoring of the assembly process is crucial for preventing costly defects and ensuring production reliability. Vision-based automated inspection offers a powerful solution for such real-time monitoring. However, due to the specialized industrial components and processes, training these models typically relies on task-specific real-world data, which is costly and labor-intensive to collect and annotate. In this paper, we propose a system that automatically generates realistic assembly sequences and further trains real-time inspection models using the synthetic data. It can be efficiently applied to a given task within an hour, requiring only CAD models and simple step descriptions. Focusing on practical challenges, our system integrates a physics-based motion generation module to capture the variance of different human assembly, designs domain-randomized rendering to deal with the environmental complexity and variation, and employs an object-detection-based step recognition module for robust sim-to-real transfer, leading to 92.4% accuracy on a real-world assembly case with 46.7%, 15.8% and 61.2% performance improvement, respectively. Overall, our system provides a practical solution for industrial assembly inspection without requiring expensive real-world data collection and annotation, with the effectiveness validated on real industrial assembly tasks.
Comments: Accepted by CASE 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.00129 [cs.CV]
(or arXiv:2607.00129v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.00129
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hui Zhang [view email] [v1] Tue, 30 Jun 2026 20:08:47 UTC (16,785 KB)
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