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

Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics