用合成数据提升金属表面缺陷检测模型性能
SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection
- 构建全流程图像生成流水线，模拟真实金属表面纹理与缺陷
- 在铣削和喷砂铝材上验证，合成数据训练模型表现接近真实数据
- 开源双数据集（真实+合成），适合缺陷检测研究者使用
机器学习方法在开发鲁棒、灵活的视觉检测系统方面展现出巨大潜力，但其性能高度依赖于训练数据的数量与多样性。由于缺陷类型多样、产品表面差异大且出现频率不均，实际采集数据往往不足，难以覆盖所有关键场景。为此，本文提出一个完整的图像合成流水线，涵盖从原始数据采集、纹理与缺陷建模、数据生成、与真实数据对比，到利用合成数据训练缺陷分割模型的全流程。该方法在铣削和喷砂铝材表面进行了详细评估。除深入解析各步骤外，还讨论了方法选择并展示机器学习结果。同时，本文提供了一个包含真实与合成图像的综合性双数据集。
原文摘要 · Abstract (English)
The use of machine learning (ML) methods for development of robust and flexible visual inspection system has shown promising. However their performance is highly dependent on the amount and diversity of training data. This is often restricted not only due to costs but also due to a wide variety of defects and product surfaces which occur with varying frequency. As such, one can not guarantee that the acquired dataset contains enough defect and product surface occurrences which are needed to develop a robust model. Using parametric synthetic dataset generation, it is possible to avoid these issues. In this work, we introduce a complete pipeline which describes in detail how to approach image synthesis for surface inspection - from first acquisition, to texture and defect modeling, data generation, comparison to real data and finally use of the synthetic data to train a defect segmentation model. The pipeline is in detail evaluated for milled and sandblasted aluminum surfaces. In addition to providing an in-depth view into each step, discussion of chosen methods, and presentation of ML results, we provide a comprehensive dual dataset containing both real and synthetic images.
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