An Incremental Unified Framework for Small Defect Inspection
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866912038607912960 |
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| author | Tang, Jiaqi Lu, Hao Xu, Xiaogang Wu, Ruizheng Hu, Sixing Zhang, Tong Cheng, Tsz Wa Ge, Ming Chen, Ying-Cong Tsung, Fugee |
| author_facet | Tang, Jiaqi Lu, Hao Xu, Xiaogang Wu, Ruizheng Hu, Sixing Zhang, Tong Cheng, Tsz Wa Ge, Ming Chen, Ying-Cong Tsung, Fugee |
| contents | Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing. Yet, many methods, tailored to specific pipelines, grapple with diverse product portfolios and evolving processes. Addressing this, we present the Incremental Unified Framework (IUF), which can reduce the feature conflict problem when continuously integrating new objects in the pipeline, making it advantageous in object-incremental learning scenarios. Employing a state-of-the-art transformer, we introduce Object-Aware Self-Attention (OASA) to delineate distinct semantic boundaries. Semantic Compression Loss (SCL) is integrated to optimize non-primary semantic space, enhancing network adaptability for novel objects. Additionally, we prioritize retaining the features of established objects during weight updates. Demonstrating prowess in both image and pixel-level defect inspection, our approach achieves state-of-the-art performance, proving indispensable for dynamic and scalable industrial inspections. Our code will be released at https://github.com/jqtangust/IUF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08917 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An Incremental Unified Framework for Small Defect Inspection Tang, Jiaqi Lu, Hao Xu, Xiaogang Wu, Ruizheng Hu, Sixing Zhang, Tong Cheng, Tsz Wa Ge, Ming Chen, Ying-Cong Tsung, Fugee Computer Vision and Pattern Recognition Artificial Intelligence Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing. Yet, many methods, tailored to specific pipelines, grapple with diverse product portfolios and evolving processes. Addressing this, we present the Incremental Unified Framework (IUF), which can reduce the feature conflict problem when continuously integrating new objects in the pipeline, making it advantageous in object-incremental learning scenarios. Employing a state-of-the-art transformer, we introduce Object-Aware Self-Attention (OASA) to delineate distinct semantic boundaries. Semantic Compression Loss (SCL) is integrated to optimize non-primary semantic space, enhancing network adaptability for novel objects. Additionally, we prioritize retaining the features of established objects during weight updates. Demonstrating prowess in both image and pixel-level defect inspection, our approach achieves state-of-the-art performance, proving indispensable for dynamic and scalable industrial inspections. Our code will be released at https://github.com/jqtangust/IUF. |
| title | An Incremental Unified Framework for Small Defect Inspection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2312.08917 |