用少量缺陷图生成逼真缺陷，提升视觉检测模型性能
DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection
- 基于微调的图像修复扩散模型，结合缺陷、物体和注意力损失
- 在MVTec AD数据集上生成高质量缺陷图，检测效果达最优
- 适合缺乏缺陷数据的工业视觉检测场景
由于缺陷数据稀缺，开发高效的视觉检测模型仍具挑战性。尽管已有图像生成模型用于合成缺陷图像，但生成高度逼真的缺陷仍困难。本文提出DefectFill，一种仅需少量参考缺陷图像即可生成真实缺陷的新方法。该方法利用微调的图像修复扩散模型，并通过自定义损失函数（包含缺陷、物体和注意力项）进行优化，能精准捕捉细节化的局部缺陷特征，并将其无缝融合到无缺陷物体中。此外，提出的低保真度选择策略进一步提升了缺陷样本质量。实验表明，DefectFill生成的缺陷图像质量高，使视觉检测模型在MVTec AD数据集上达到当前最优性能。
原文摘要 · Abstract (English)
Developing effective visual inspection models remains challenging due to the scarcity of defect data. While image generation models have been used to synthesize defect images, producing highly realistic defects remains difficult. We propose DefectFill, a novel method for realistic defect generation that requires only a few reference defect images. It leverages a fine-tuned inpainting diffusion model, optimized with our custom loss functions incorporating defect, object, and attention terms. It enables precise capture of detailed, localized defect features and their seamless integration into defect-free objects. Additionally, our Low-Fidelity Selection method further enhances the defect sample quality. Experiments show that DefectFill generates high-quality defect images, enabling visual inspection models to achieve state-of-the-art performance on the MVTec AD dataset.
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