As automation advances in manufacturing, the demand for precise and sophisticated defect detection technologies grows. Existing vision models for defect recognition methods are insufficient for handling the complexities and variations of defects in contemporary manufacturing settings. These models especially struggle in scenarios involving limited or imbalanced defect data. In this work, we introduce MemoryMamba, a novel memory-augmented state space model (SSM), designed to overcome the limitations of existing defect recognition models. MemoryMamba integrates the state space model with the memory augmentation mechanism, enabling the system to maintain and retrieve essential defect-specific information in training. Its architecture is designed to capture dependencies and intricate defect characteristics, which are crucial for effective defect detection. In the experiments, MemoryMamba was evaluated across four industrial datasets with diverse defect types and complexities. The model consistently outperformed other methods, demonstrating its capability to adapt to various defect recognition scenarios.

翻译：随着制造业自动化的不断发展，对精确且复杂的缺陷检测技术的需求日益增长。现有的视觉缺陷识别模型难以应对当代制造场景中缺陷的复杂性与多样性，尤其在缺陷数据有限或不平衡的情况下表现欠佳。本文提出了一种新型记忆增强状态空间模型（SSM）——MemoryMamba，旨在克服现有缺陷识别模型的局限性。MemoryMamba将状态空间模型与记忆增强机制相结合，使系统能够在训练过程中维持并检索关键的缺陷特定信息。其架构专门设计用于捕捉缺陷的依赖关系及复杂特征，这对实现有效缺陷检测至关重要。实验环节中，MemoryMamba在四个包含不同缺陷类型与复杂度的工业数据集上进行了评估。该模型始终优于其他方法，展现出其适应各类缺陷识别场景的能力。