Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. Furthermore, we introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images, even when working with limited datasets. The synthetic images generated by Defect-Gen significantly enhance the efficacy of defect inspection models. Overall, The Defect Spectrum dataset demonstrates its potential in defect inspection research, offering a solid platform for testing and refining advanced models.

翻译：缺陷检测在闭环制造系统中至关重要。然而，现有缺陷检测数据集往往缺乏实际应用所需的精确性和语义粒度。本文提出缺陷谱（Defect Spectrum），这是一个全面的基准数据集，为广泛的工业缺陷提供了精确、语义丰富且大规模的标注。基于四个关键工业基准数据集，本工作细化了现有标注并引入了丰富的语义细节，能够在单张图像中区分多种缺陷类型。此外，我们提出了Defect-Gen——一个两阶段基于扩散的生成器，即使在有限数据集条件下也能生成高质量、多样化的缺陷图像。Defect-Gen生成的合成图像显著提升了缺陷检测模型的性能。总体而言，缺陷谱数据集展现了其在缺陷检测研究中的潜力，为测试和完善先进模型提供了坚实的平台。