arXiv 2406.115072024-06-17cs.CV

用于多类别工业图像异常检测的先验正常性提示 Transformer

Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection

Tsinghua University（清华大学）；Huazhong University of Science and Technology（华中科技大学）；Beijing Institute of Technology（北京理工大学）

Haiming Yao, Yunkang Cao, Wei Luo, Weihang Zhang, Wenyong Yu, Weiming Shen

备注：: Accepted by IEEE Transactions on Industrial Informatics

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AI总结：

针对多类别工业异常检测中重建模型的恒等捷径学习问题，本文提出 PNPT，通过先验正常性提示和双流重建架构提升正常与异常样本的区分性能。

摘要：

图像异常检测在工业检测中发挥着关键作用。传统方法通常需要针对特定类别使用不同模型，导致高昂的部署成本。这引发了对多类别异常检测的关注，即为多个类别开发一个统一模型。然而，将传统方法（尤其是基于重建的模型）直接应用于多类别场景会遇到恒等捷径学习等挑战，阻碍对正常与异常实例的有效区分。为解决该问题，本研究提出用于多类别图像异常检测的先验正常性提示 Transformer（Prior Normality Prompt Transformer，PNPT）方法。PNPT 策略性地引入正常语义提示，以缓解“恒等映射”问题。这包括将先验正常性提示集成到重建过程中，从而形成一个双流模型。这一创新架构将正常先验语义与异常样本相结合，实现同时基于先验知识和样本内在特征的双流重建。PNPT 包含四个必要模块：类别特定正常性提示池（Class-Specific Normality Prompting Pool，CS-NPP）、分层块嵌入（Hierarchical Patch Embedding，HPE）、语义对齐耦合编码（Semantic Alignment Coupling Encoding，SACE）以及上下文语义条件解码（Contextual Semantic Conditional Decoding，CSCD）。在多种基准数据集和真实工业应用上的实验验证突显了 PNPT 在多类别工业异常检测中的优越性能。

英文摘要：

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multi-class scenarios encounters challenges such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the Prior Normality Prompt Transformer (PNPT) method for multi-class image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the "identical mapping" problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: Class-Specific Normality Prompting Pool (CS-NPP), Hierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding (SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multi-class industrial anomaly detection.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。