用掩码控制生成钢表面缺陷，无需像素标注即可提升检测模型性能。
DefFiller: Mask-Conditioned Diffusion for Salient Steel Surface Defect Generation
- 基于掩码条件的扩散模型生成缺陷图像，无需精细标注。
- 在SD-Saliency-900数据集上生成缺陷图像与掩码高度匹配。
- 显著提升基于显著性的缺陷检测模型性能，适合工业质检场景。
当前基于显著性的缺陷检测方法在工业场景中展现出潜力，但钢铁生产环境中缺陷的不可预测性导致数据集构建困难，制约模型性能。现有生成式数据增强方法常需像素级标注，耗时且成本高。为此，我们提出DefFiller，一种基于布局到图像扩散模型的掩码条件缺陷生成方法。DefFiller生成与掩码条件配对的缺陷样本，无需像素级标注，可直接用于模型训练。我们还构建了评估框架，用于衡量生成样本质量及其对检测性能的影响。在SD-Saliency-900数据集上的实验表明，DefFiller生成的缺陷图像质量高，能准确匹配给定掩码条件，显著提升基于增强数据集训练的显著性缺陷检测模型性能。
原文摘要 · Abstract (English)
Current saliency-based defect detection methods show promise in industrial settings, but the unpredictability of defects in steel production environments complicates dataset creation, hampering model performance. Existing data augmentation approaches using generative models often require pixel-level annotations, which are time-consuming and resource-intensive. To address this, we introduce DefFiller, a mask-conditioned defect generation method that leverages a layout-to-image diffusion model. DefFiller generates defect samples paired with mask conditions, eliminating the need for pixel-level annotations and enabling direct use in model training. We also develop an evaluation framework to assess the quality of generated samples and their impact on detection performance. Experimental results on the SD-Saliency-900 dataset demonstrate that DefFiller produces high-quality defect images that accurately match the provided mask conditions, significantly enhancing the performance of saliency-based defect detection models trained on the augmented dataset.
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