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 \url{https://github.com/jqtangust/IUF}.

翻译：人工智能驱动的缺陷检测在工业制造中至关重要。然而，许多针对特定流水线定制的检测方法难以适应多样化的产品组合和不断演进的工艺。为此，我们提出了增量统一框架（Incremental Unified Framework, IUF），该框架能在持续集成流水线中新对象时减少特征冲突问题，从而在对象增量学习场景中展现优势。通过采用最先进的Transformer架构，我们引入了对象感知自注意力机制（Object-Aware Self-Attention, OASA）以清晰界定语义边界。同时，集成语义压缩损失（Semantic Compression Loss, SCL）优化非主语义空间，增强网络对新对象的适应性。此外，我们在权重更新过程中优先保留已有对象的特征。该方法在图像级和像素级缺陷检测任务中均展现出卓越性能，达到当前最优水平，成为动态、可扩展工业检测不可或缺的技术方案。我们的代码将发布于\url{https://github.com/jqtangust/IUF}。