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Received：31 October 2024，
Revised：17 February 2025，
Accepted：2025-02-25，
Published Online：26 February 2025，
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目的
2
传统异常检测方法在工业产品缺陷判别中仅关注当前任务，从而导致在接受新任务训练时会灾难性地遗忘以前学过的知识。鉴于现实工业场景中对异常检测模型的灵活性和持续适应性的需求，结合连续学习方法提出一种适配器增强的双阶段连续缺陷判别方法（adapter-enhanced two-stage continual defect detection，AETS）以实现连续异常检测任务。
方法
2
首先，在AdaptFormer基础上引入外部注意力机制，增强模型对顺序任务中的全局依赖关系的捕捉能力，以提升对新任务的泛化性能。其次，在视觉转换器（vision Transformer，ViT）预训练模型的基础上结合高效微调技术，采用双阶段训练策略，即在适应阶段，通过全量微调缓解自然图像与工业图像之间的域差异；在高效微调阶段，通过适配器增强模块提升模型对新任务的适应性，同时冻结大部分参数以保留对旧任务的记忆，从而缓解灾难性遗忘问题。此外，还提出遗忘波动率（forgetting fluctuation rate， FFR）这一新的连续学习评价指标，用于量化模型在整个学习过程中遗忘波动情况，以检验模型在工业场景中的适用性和稳定性。
结果
2
在MVTec-MCIL、MVTec-SCIL和MVTec+MTD数据集上进行实验，AETS的ACC值分别达到84.21%、89.16%和78.49%，相较于5种连续学习方法，AETS具有最佳的ACC、FM值和最小的训练参数量，相较于6种先进高效微调方法其FFR值达到最佳。消融实验选取缩放因子及确定适配器增强模块结构，以实现模型可塑性与稳定性的最佳平衡。
结论
2
所提出的AETS方法通过构建适配器增强模块，充分利用预训练模型的特征表达能力，双阶段训练策略能够捕捉与任务相关的特征，显著增强模型在连续工业缺陷判别任务中的适应性和泛化性。
Objective
2
Due to the difficulty in obtaining defect samples in industrial scenarios， many existing anomaly detection algorithms rely solely on normal samples during the training phase. These methods demonstrate strong performance in industrial defect detection by training a single model tailored to specific product types. However， traditional anomaly detection methods mainly focus on the current task in industrial product defect discrimination， which often results in catastrophic forgetting knowledge of previously learned tasks when the model is trained on new tasks. As product objects and defect types continuously change and diversify in real-world industrial environments， anomaly detection models need to possess greater flexibility and continuous adaptation capability to cope with new tasks and data. Therefore， this paper proposes an adapter-enhanced two-stage continual defect detection （AETS） method tailored for continuous anomaly detection tasks.
Method
2
Firstly， building upon the AdaptFormer framework， this paper introduces an external attention mechanism to augment the model's capability in capturing global dependencies across sequential tasks， thereby enhancing generalization performance on new tasks. This enhancement is crucial in industrial anomaly detection， enabling the AETS model to adapt to diverse and complex defect types across different products. Furthermore， combining parameter-efficient tuning technique with vision Transformer （ViT） pre-trained models， the AETS framework incorporates two primary stages of training to enhance the model’s ability to retain previously learned knowledge while effectively adapting to new tasks. The first stage focuses on adaptation， where a full parameter fine-tuning strategy is employed to mitigate the domain shift between natural and industrial images. This is critical as the differences in feature distributions between these two domains can severely hinder the performance of industrial anomaly detection models when directly applied to industrial datasets. To further enhance the adaptation process， the AETS model integrates an external attention mechanism into the existing AdaptFormer architecture. This integration allows the model to capture long-range dependencies across sequential tasks， which is vital for maintaining performance across different domains and tasks. This incorporation of external attention enables the model to better generalize to new tasks by improving the representation learning of global contextual information， which not only improves the model's ability to attend to relevant features but also enhances its capacity to model global contextual information. By refining the model's representation learning， the external attention mechanism improves the flexibility and robustness. In the second stage， known as efficient fine-tuning， the AETS method utilizes adapter modules to selectively fine-tune a minimal number of parameters， thereby reducing computational overhead while retaining essential knowledge from previously learned tasks. By freezing most of the parameters， the model is able to preserve knowledge from prior tasks and mitigate catastrophic forgetting， which is a common challenge in continual learning settings. The adapter-enhanced module is designed to enhance the model’s capacity for handling new tasks without overwriting previously learned information， ensuring that the model can continuously adapt to evolving data distributions in industrial environments. Forgetting measure （FM） only evaluates the forgetting compared to the maximum performance achieved in the past for each task at the completion of the final model training. In order to provide a comprehensive assessment of the catastrophic forgetting problem， this paper introduces a novel evaluation metric called forgetting fluctuation rate （FFR）， specifically designed for continuous learning scenarios. FFR is used for quantifying the extent of forgetting during the learning process and can provide a more granular assessment of model stability across sequential tasks. By measuring the fluctuation in forgetting over time， FFR helps evaluate how well the model retains its knowledge while learning new tasks， offering a more robust evaluation of continual learning performance in industrial defect detection scenarios. When the FFR value is low， it indicates that the model's forgetting is relatively smooth， with less performance fluctuation. Conversely， the higher the value， the greater the fluctuation in forgetting.
Results
2
We conduct extensive experiments using three benchmark datasets from the MVTecAD dataset， which are specifically divided to evaluate the model's performance in different industrial defect detection scenarios. The AETS method demonstrates significant improvements in average accuracy （ACC）， achieving scores of 84.21%， 89.16%， and 78.49% on the MVTec-MCIL， MVTec-SCIL， and MVTec+MTD datasets， respectively. These results indicate that AETS outperforms other continual learning models， especially in terms of efficiency and resource utilization. Compared to five continuous learning methods， AETS exhibits the best performance in terms of ACC and FM metrics， with the smallest number of training parameters. Furthermore， when compared to six state-of-the-art parameter-efficient tuning methods， AETS achieves optimal FFR values. This demonstrates that our method not only improves model accuracy but also provides a lightweight solution that is more practical for real-world industrial applications. We perform ablation studies to further validate the effectiveness of our proposed method. Specifically， the ablation experiments focus on selecting the optimal scaling factor and determining the best structure for the adapter enhancement module. These experiments are crucial in achieving a balanced trade-off between the model's plasticity， enabling it to adapt to new tasks， and its stability， preserving previously learned knowledge.
Conclusion
2
This paper presents a novel continual anomaly detection method， AETS， which leverages the powerful feature representation capability of pre-trained models combined with adapter-enhanced modules to improve continual learning performance in industrial defect detection tasks. By adopting a two-stage training process， AETS effectively addresses domain shift issues between natural and industrial images， and efficiently captures task-relevant features through parameter-efficient fine-tuning， which not only mitigates catastrophic forgetting but also enhances the training efficiency of the model. Additionally， the introduction of FFR further validates the model's robustness and adaptability in dynamic industrial environments. The experimental results demonstrate that AETS can achieve competitive performance across various benchmark datasets. It not only maintains high detection accuracy but also significantly enhances the model's stability and plasticity in the context of continuous learning. By mitigating catastrophic forgetting， AETS effectively enables the model to adapt to new tasks without compromising previously learned knowledge. These advantages make AETS particularly well-suited for real-world industrial applications， where both accuracy and the ability to continuously learn from evolving data are essential for practical deployment and long-term performance.
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