  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}.
