Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
This work addresses the problem of limited data quality for defect inspection in manufacturing, though it is incremental as it builds on existing benchmarks.
The paper tackles the lack of precision and semantic granularity in existing defect inspection datasets by introducing the Defect Spectrum, a comprehensive benchmark with refined annotations and rich semantic details, and Defect-Gen, a diffusion-based generator that creates synthetic images to enhance model efficacy.
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.