Li Zhixing, Yang Zan, Zhang Lijie, Yang Lie, Liu Jiansheng
School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China.
Tellhow Sci-Tech Co., Ltd., Nanchang 330096, China.
Sensors (Basel). 2025 Aug 13;25(16):5016. doi: 10.3390/s25165016.
In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy for structural anomalies such as cracks and scratches. However, logical anomalies typically appear normal within local regions of an image and are difficult to represent well by the anomaly score map, requiring the model to possess the capability to extract global context features. To address this challenge while balancing the detection of both structural and logical anomalies, this paper proposes a lightweight anomaly detection framework built upon EfficientAD. This framework integrates the reconstruction difference constraint (RDC) and a logical anomaly detection module. Specifically, the original EfficientAD relies on the coarse-grained reconstruction difference between the student and the autoencoder to detect logical anomalies; but, false detection may be caused by the local fine-grained reconstruction difference between the two models. RDC can promote the consistency of the fine-grained reconstruction between the student and the autoencoder, thereby effectively alleviating this problem. Furthermore, in order to detect anomalies that are difficult to represent by feature maps more effectively, the proposed logical anomaly detection module extracts and aggregates the context features of the image, and combines the feature-based method to calculate the overall anomaly score. Extensive experiments demonstrate our method's significant improvement in logical anomaly detection, achieving 94.2 AU-ROC on MVTec LOCO, while maintaining strong structural anomaly detection performance at 98.4 AU-ROC on MVTec AD. Compared to the baseline, like EfficientAD, our framework achieves a state-of-the-art balance between both anomaly types.
在智能制造领域，图像异常检测在自动化产品质量检测中起着关键作用。大多数现有的异常检测方法擅长捕捉图像的局部特征，对于诸如裂缝和划痕等结构异常能够实现较高的检测精度。然而，逻辑异常在图像的局部区域通常看起来是正常的，并且难以通过异常分数图很好地表示，这就要求模型具备提取全局上下文特征的能力。为了在平衡结构异常和逻辑异常检测的同时应对这一挑战，本文提出了一种基于EfficientAD构建的轻量级异常检测框架。该框架集成了重建差异约束（RDC）和逻辑异常检测模块。具体而言，原始的EfficientAD依赖于学生模型和自动编码器之间的粗粒度重建差异来检测逻辑异常；但是，这两个模型之间的局部细粒度重建差异可能会导致误检。RDC可以促进学生模型和自动编码器之间细粒度重建的一致性，从而有效缓解这个问题。此外，为了更有效地检测难以通过特征图表示的异常，所提出的逻辑异常检测模块提取并聚合图像的上下文特征，并结合基于特征的方法来计算整体异常分数。大量实验表明，我们的方法在逻辑异常检测方面有显著改进，在MVTec LOCO上达到了94.2的AU-ROC，同时在MVTec AD上以98.4的AU-ROC保持了强大的结构异常检测性能。与像EfficientAD这样的基线相比，我们的框架在两种异常类型之间实现了当前最优的平衡。
