ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

2026-07-29

AI Summary

This study addresses the challenge of surface scratch detection in industrial quality inspection, where performance is often hindered by the scarcity of real annotated data. To overcome this limitation, the authors propose a procedural synthetic data generation pipeline based on BlenderProc, which leverages material modeling, multi-camera configurations, and domain randomization to efficiently produce large-scale scratch images with COCO-format annotations. This approach enables training lightweight detection models—such as YOLOX, YOLOv8, and LW-DETR—without requiring extensive real-world labeled data, thereby facilitating deployment on edge devices. Experimental results demonstrate that fine-tuning models pretrained on synthetic data significantly outperforms training solely on real data across diverse materials. Moreover, when real annotations are extremely limited, a hybrid training strategy effectively recovers detection performance for both convolutional and Transformer-based architectures.

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, 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.

Problem

• surface scratch detection
• defect detection
• data scarcity
• industrial quality control
• synthetic data

Innovation

• synthetic data generation
• surface scratch detection
• domain randomization
• procedural rendering
• few-shot defect detection