Towards Automated Assembly Quality Inspection with Synthetic Data and Domain Randomization

Xiaomeng Zhu, Jacob Henningsson, Duruo Li, Pär Mårtensson, Lars Hanson, Mårten Björkman, Atsuto Maki; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 1406-1414

Assembly quality inspection plays a vital role in manufacturing, where correct part placement and alignment directly affect product reliability. While deep learning-based object detection offers a promising solution for automatic assembly quality inspection, it is hindered by data scarcity. Training on synthetic data with Domain Randomization (DR) helps address this challenge, yet existing DR methods focus on generating individual objects and do not capture the relational structure needed for assembly inspection. In this paper, we identify two key factors for effective synthetic data generation in assembly inspection: preserving spatial relationships between components and providing part-level textures and annotations. We propose an Assembly-Specific Generation Scheme that incorporates these factors into a state-of-the-art DR pipeline. To evaluate its impact, we introduce SIP2A-OD, a new object detection dataset comprising two real-world assembly use cases collected under varied manufacturing conditions. We train a YOLOv12 model on synthetic data generated by our pipeline and test it on real data from the SIP2A-OD dataset. Compared to the baseline pipeline designed for individual object detection, our method improves mAP@50 by more than 15% in both use cases. These results demonstrate the effectiveness of our scheme and its potential for broader applications in industrial assembly inspection without the need for manual data collection or annotation.