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) that 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
this https URL

通过应用人工智能技术的增量统一框架（IUF）和对象感知自注意（OASA）等方法，该研究论文提出了一种用于工业制造中人工智能驱动的缺陷检测的方法，具有卓越的性能，适用于动态和可扩展的工业检查。