ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection
Quick summary
arXiv:2607.27065v2 Announce Type: cross Abstract: While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this task challenging. This paper presents a procedural rendering pipeline that generates large-scale annotated synthetic training data using BlenderProc, with configurable material appearance, camera modes, and domain randomization, producing automatic COCO-format annotations. To show the potential of our approach, we evaluate four training strategies, namely synthetic-only
Key takeaways
- arXiv:2607.27065v2 Announce Type: cross Abstract: While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this task challenging.
- This paper presents a procedural rendering pipeline that generates large-scale annotated synthetic training data using BlenderProc, with configurable material appearance, camera modes, and domain randomization, producing automatic COCO-format annotations.
- To show the potential of our approach, we evaluate four training strategies, namely synthetic-only
Why it matters
“ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.