SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection
Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly. We present SynSur, an end-to-end pipeline for synthetic defect generation and automatic annotation, designed to reduce the manual effort and data scarcity that limit deployed inspection systems. The pipeline combines Vision-Language-Model-based prompt construction, LoRA-adapted diffusion, mask-guided inpainting, metric-based sample filtering, and automatic label derivation. We evaluate SynSur on BSData (pitting defects on ball screw drives) and the scratch subset of MSD, reporting downstream detec...
| Authors | Paul Julius Kühn, Mika Pommeranz, Arjan Kuijper et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 April 2026 |
| Last updated at source | 22 July 2026 |
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