这篇论文用BlenderProc生成合成划痕数据，微调策略比纯真实数据训练效果更好，适合做工业质检的场景。
该论文提出基于BlenderProc的程序化渲染管道ScratchSim，可生成大规模带COCO格式标注的合成划痕数据。通过在两个不同材质物体上评估YOLOX、YOLO26和LW-DETR三种轻量级检测器，对比了纯合成、纯真实、混合及从合成权重微调四种策略。实验表明，从合成权重微调始终优于仅用真实数据训练，混合训练可在真实数据稀缺时有效恢复性能。该方法无需大量人工标注真实数据即可实现可扩展的缺陷检测，适用于工业质检边缘设备。
ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection
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, real-only, mixed, and fine-tuning from synthetic weights, across two objects with different material properties and three lightweight edge-deployable detectors, YOLOX, YOLO26, and LW-DETR. Our evaluation show that fine-tuning from synthetic weights consistently outperforms real-only training, and that mixed training effectively recovers performance under scarce real-data conditions, with findings validated across both convolutional and transformer-based architectures. The proposed approach enables scalable defect detection without the burden of large real annotated datasets, making it practical for on-device industrial inspection. The pipeline scripts, 3D model, and both synthetic and real annotated scratch datasets for a glossy toy Ferrari car will be made available through the project website upon acceptance.