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SynSur addresses the critical data scarcity in industrial defect inspection by leveraging diffusion models and vision-language prompts to generate high-quality synthetic training sets without manual labeling. While synthetic-only training cannot replace real-world data, augmenting limited datasets with these generated samples consistently improves detection performance for downstream object detectors like YOLO variants. This approach offers a practical pathway to overcome annotation bottlenecks in specialized manufacturing environments where collecting balanced defect images is traditionally slow and expensive.
SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection
arXiv:2604.26633v2 Announce Type: replace-cross Abstract: 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 detection performance across YOLOX, YOLOv26, and LW-DETR under real-only, synthetic-only, mixed, and union training regimes. The full generation pipeline processes a candidate pool of 1,000 images and delivers a filtered, annotated synthetic dataset with no manual labeling effort. Synthetic-only training does not replace real data; however, augmenting the full real set with synthetic samples yields consistent AP gains in selected configurations, and augmenting scarce real sets substantially reduces the performance gap to full real-data training. The cross-domain study on MSD confirms pipeline portability while highlighting the importance of domain-specific adaptation and annotation quality control. SynSur demonstrates that diffusion-based defect synthesis is a practically viable tool for strengthening industrial inspection pipelines, particularly where labeled data collection is the primary bottleneck.
Abstract: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 detection performance across YOLOX, YOLOv26, and LW-DETR under real-only, synthetic-only, mixed, and union training regimes. The full generation pipeline processes a candidate pool of 1,000 images and delivers a filtered, annotated synthetic dataset with no manual labeling effort. Synthetic-only training does not replace real data; however, augmenting the full real set with synthetic samples yields consistent AP gains in selected configurations, and augmenting scarce real sets substantially reduces the performance gap to full real-data training. The cross-domain study on MSD confirms pipeline portability while highlighting the importance of domain-specific adaptation and annotation quality control. SynSur demonstrates that diffusion-based defect synthesis is a practically viable tool for strengthening industrial inspection pipelines, particularly where labeled data collection is the primary bottleneck.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2604.26633 [cs.CV]
(or arXiv:2604.26633v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2604.26633 Focus to learn more arXiv-issued DOI via DataCite
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