SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

2026-04-29

AI Summary

Industrial defect detection faces significant challenges due to the scarcity, high annotation cost, and imbalanced distribution of real-world labeled data. This work proposes the first end-to-end generative framework that innovatively integrates vision-language model (VLM)-guided prompting, LoRA-finetuned Stable Diffusion, mask-guided inpainting, and automatic filtering with DreamSim and CLIPScore for realistic synthetic defect image generation with precise annotations. Experiments on the BSData and MSD datasets demonstrate that augmenting real data with the generated images during training of detectors such as YOLOv6, YOLOX, and LW-DETR maintains or even slightly improves detection performance, validating the method’s effectiveness and practical utility in cross-domain industrial quality inspection.

Abstract

The bottleneck in learning-based industrial defect detection is often limited not by model capacity, but by the scarcity of labeled defect data: defects are rare, annotations are expensive, and collecting balanced training sets is slow. We present an end-to-end pipeline for synthetic defect generation and annotation, combining Vision-Language-Model-based prompts, LoRA-adapted diffusion, mask-guided inpainting, and sample filtering with automatic label derivation, and demonstrates the potential of real data with realistic synthetic samples to overcome data scarcity. The evaluation is conducted on, a challenging dataset of pitting defects on ball screw drives, and then on a subset of the Mobile phone screen surface defect segmentation dataset (MSD) dataset to test cross-domain transfer. Beyond downstream detector performance, we analyze key stages of the pipeline, including prompt construction, LoRA selection, and sample filtering with DreamSim and CLIPScore, to understand which synthetic samples are both realistic and useful. Experiments with YOLOv26, YOLOX, and LW-DETR show that synthetic-only training does not replace real data. When combined with real data, synthetic defects can preserve performance and yield modest gains in selected BSData training regimes. The MSD transfer study shows that the overall pipeline structure carries over to a second industrial inspection domain, while also highlighting the importance of domain-specific adaptation and annotation-quality control. Overall, the paper provides an end-to-end assessment of diffusion-based industrial defect synthesis and shows that its strongest value lies in strengthening scarce real datasets rather than substituting for them.

Problem

• industrial defect detection
• data scarcity
• synthetic data generation
• labeled defect data
• surface defects

Innovation

• synthetic defect generation
• diffusion model
• LoRA adaptation
• mask-guided inpainting
• vision-language prompting