In recent years, deep learning-based approaches for industrial surface defect detection have shown great promise. To address the domain shift issue among data from different sources in the industrial domain, we present a novel plug-and-play Style Adaptation (SA) module, which endows the equipped defect detector with the capability to exhibit robustness to diverse styles present within the samples. This module effectively leverages datasets sourced from diverse origins while possessing congruent data types. In contrast to other domain adaptation approaches lacking well-defined domain delineations, the SA module generates representations characterized by distinct practical implications and precise mathematical formulations. Moreover, incorporating attention mechanisms reduces the need for manual intervention, allowing the module to focus autonomously on crucial branches in it. Experimental results demonstrate the superior efficacy of our approach compared to state-of-the-art techniques. Furthermore, an authentic dataset from various manufacturers is publicly available for deep learning research and industrial applications. Access the dataset at:

中文翻译：

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风格适应模块：增强探测器对表面缺陷检测中制造商间差异的鲁棒性

近年来，基于深度学习的工业表面缺陷检测方法显示出了巨大的前景。为了解决工业领域不同来源的数据之间的域转移问题，我们提出了一种新颖的即插即用风格适应（SA）模块，该模块赋予所配备的缺陷检测器能够对工业领域中存在的多种风格表现出鲁棒性。样品。该模块有效地利用来自不同来源的数据集，同时拥有一致的数据类型。与缺乏明确定义的域划分的其他域适应方法相比，SA 模块生成的表示具有独特的实际含义和精确的数学公式。此外，纳入注意力机制减少了手动干预的需要，使模块能够自主地关注其中的关键分支。实验结果表明，与最先进的技术相比，我们的方法具有卓越的功效。此外，来自不同制造商的真实数据集是公开的，可用于深度学习研究和工业应用。访问数据集：