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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.17316 |
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| _version_ | 1866917727953747968 |
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| author | Yang, Shuai Chen, Zhifei Chen, Pengguang Fang, Xi Liang, Yixun Liu, Shu Chen, Yingcong |
| author_facet | Yang, Shuai Chen, Zhifei Chen, Pengguang Fang, Xi Liang, Yixun Liu, Shu Chen, Yingcong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17316 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics Yang, Shuai Chen, Zhifei Chen, Pengguang Fang, Xi Liang, Yixun Liu, Shu Chen, Yingcong Computer Vision and Pattern Recognition 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. |
| title | Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.17316 |