Hybrid synthetic data generation with domain randomization enables zero-shot vision-based part inspection under extreme class imbalance

Machine learning, particularly deep learning, is transforming industrial quality inspection. Yet, training robust machine learning models typically requires large volumes of high-quality labeled data, which are expensive, time-consuming, and labor-intensive to obtain in manufacturing. Moreover, defective samples are intrinsically rare, leading to severe class imbalance that degrades model performance. These data constraints hinder the adoption of machine learning-based quality inspection methods in real production environments. Synthetic data generation (SDG) offers a promising solution by enabling the creation of large, balanced, and fully annotated datasets in an efficient, cost-effective, and scalable manner. This paper presents a hybrid SDG framework that integrates simulation-based rendering, domain randomization, and real background compositing to enable annotation-free computer vision-based industrial part inspection and zero-shot deployment on real parts. In our implementation, the SDG pipeline generates 12,960 labeled, balanced images in one hour, demonstrating its efficiency and scalability in addressing manufacturing data bottlenecks. A two-stage architecture utilizing a YOLOv8n backbone for object detection and MobileNetV3-small for quality classification is trained exclusively on synthetic data and evaluated on 300 real industrial parts. The proposed approach achieves a mean Average Precision (mAP) of 0.995 at an intersection-over-union threshold of 0.5 (mAP@0.5) for object detection and 96% overall accuracy with 90.5% balanced accuracy for quality classification, indicating robust performance under severe class imbalance. Compared with few-shot baselines trained on limited real data, the SDG approach improves balanced accuracy from 50% to 91%. These results demonstrate that the proposed method enables annotation-free, scalable, and robust quality inspection for real-world manufacturing applications.