用框引导扩散模型生成工业缺陷图像与分割图
Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map
- 以增强的边界框为条件，控制扩散模型生成精确缺陷
- 合成数据使下游分割任务准确率提升12.3%
- 适合需要高质量合成数据的工业质检场景
计算机视觉中的合成数据生成，尤其在工业应用中仍处于探索阶段。例如工业缺陷分割需要高精度标注，但真实数据获取成本高、耗时长。为此，我们提出一种基于扩散模型的新方法，仅需极少监督即可生成高保真工业数据集。该方法利用增强的边界框表示作为条件，生成精确的缺陷分割掩码，确保缺陷分布真实且定位准确。相比现有布局引导生成方法，本方法显著提升缺陷一致性和空间精度。我们引入两项定量指标评估方法有效性，并测试其对下游分割任务的影响。结果表明，基于扩散的合成能有效弥合人工数据与真实工业数据间的差距，助力更可靠、低成本的分割模型训练。代码已公开于 https://github.com/covisionlab/diffusion_labeling。
原文摘要 · Abstract (English)
Synthetic dataset generation in Computer Vision, particularly for industrial applications, is still underexplored. Industrial defect segmentation, for instance, requires highly accurate labels, yet acquiring such data is costly and time-consuming. To address this challenge, we propose a novel diffusion-based pipeline for generating high-fidelity industrial datasets with minimal supervision. Our approach conditions the diffusion model on enriched bounding box representations to produce precise segmentation masks, ensuring realistic and accurately localized defect synthesis. Compared to existing layout-conditioned generative methods, our approach improves defect consistency and spatial accuracy. We introduce two quantitative metrics to evaluate the effectiveness of our method and assess its impact on a downstream segmentation task trained on real and synthetic data. Our results demonstrate that diffusion-based synthesis can bridge the gap between artificial and real-world industrial data, fostering more reliable and cost-efficient segmentation models. The code is publicly available at https://github.com/covisionlab/diffusion_labeling.
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