  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.
