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，现有用于缺陷检测的数据集往往缺乏实际应用所需的精度和语义粒度。本文提出Defect Spectrum（缺陷谱），这是一个全面的基准数据集，为各类工业缺陷提供了精确、语义丰富且大规模标注。基于四个关键工业基准，本数据集精炼了现有标注，并引入丰富的语义细节，能够区分单张图像中的多种缺陷类型。此外，我们提出Defect-Gen（缺陷生成器），这是一种基于扩散的两阶段生成器，旨在即使面对有限数据集时也能生成高质量且多样化的缺陷图像。Defect-Gen生成的合成图像显著提升了缺陷检测模型的效果。总体而言，Defect Spectrum数据集在缺陷检测研究中展现出潜力，为测试和优化先进模型提供了坚实平台。