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.

自动化制造中的缺陷检测技术需求增长，但现有视觉模型在处理复杂的制造场景中的缺陷识别方法方面不足。本研究提出了 MemoryMamba，一种新颖的记忆增强状态空间模型 (SSM)，旨在克服现有缺陷识别模型的局限性。MemoryMamba 集成了状态空间模型和记忆增强机制，使系统能够在训练中保持和检索重要的缺陷特定信息。实验结果显示，MemoryMamba 在各种缺陷识别场景中表现出色，优于其他方法。