发表机构
Fraunhofer IGD; TU Darmstadt（弗劳恩霍夫计算机图形学研究所; 达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于BlenderProc的程序合成数据流水线ScratchSim，通过四种训练策略验证其在表面划痕检测中的有效性，可缓解真实标注数据稀缺问题，适用于设备端工业检测。
AI 中文摘要
表面划痕等自动化缺陷检测是工业质量控制的重要环节，但标注缺陷数据的稀缺性使该任务颇具挑战性。本文提出一种程序渲染流水线，利用BlenderProc生成大规模带标注的合成训练数据，该流水线支持配置材质外观、相机模式与域随机化，可自动生成COCO格式标注。为验证方法潜力，我们在两种不同材质属性的物体、三款轻量边缘可部署检测器YOLOX、YOLO26与LW-DETR上，评估仅合成数据、仅真实数据、混合数据、基于合成权重微调这四种训练策略。评估结果显示，基于合成权重微调的表现始终优于仅真实数据训练，混合训练在真实数据稀缺条件下可有效恢复性能，该结论在卷积与Transformer架构上均得到验证。所提方法无需依赖大量真实标注数据集即可实现可扩展的缺陷检测，适用于设备端工业检测，接受后将通过项目网站提供流水线脚本、3D模型及光泽玩具法拉利汽车的合成与真实标注划痕数据集。
英文摘要
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 for generating synthetic scratches, 3D model, and both the synthetic and real annotated scratch datasets for a glossy toy Ferrari car are publicly available at https://github.com/saptarshineil/ScratchSim.
