用自适应掩码修复提升工业缺陷检测，让模型更懂正常区域上下文。
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization
- 用多尺度语义特征做重建目标，替代传统像素级修复。
- 在MVTec AD和BTAD上实现95%以上检测准确率，推理快至10ms/图像。
- 适合实时工业质检，尤其对小缺陷、复杂纹理场景表现好。
无监督视觉异常检测对提升工业生产质量与效率至关重要。重建类方法因简单有效而广受青睐，其核心在于异常区域的恢复能力，但现有方法未能充分解决此问题。为此，本文提出一种新型自适应掩码修复网络（AMI-Net），从自适应掩码修复视角出发。不同于传统方法将非语义像素作为重建目标，本方法利用预训练网络提取多尺度语义特征作为重建目标。针对工业缺陷的多尺度特性，引入随机位置与数量的掩码训练策略。进一步设计一种创新的自适应掩码生成器，可精准遮盖异常区域同时保留正常区域。模型借此利用可见正常区域的全局上下文信息，有效抑制缺陷重建。在MVTec AD与BTAD工业数据集上的大量实验验证了方法的有效性。AMI-Net展现出优异的实时性能，在检测精度与速度间取得良好平衡，极具工业应用价值。代码已公开于：https://github.com/luow23/AMI-Net。
原文摘要 · Abstract (English)
Unsupervised visual anomaly detection is crucial for enhancing industrial production quality and efficiency. Among unsupervised methods, reconstruction approaches are popular due to their simplicity and effectiveness. The key aspect of reconstruction methods lies in the restoration of anomalous regions, which current methods have not satisfactorily achieved. To tackle this issue, we introduce a novel \uline{A}daptive \uline{M}ask \uline{I}npainting \uline{Net}work (AMI-Net) from the perspective of adaptive mask-inpainting. In contrast to traditional reconstruction methods that treat non-semantic image pixels as targets, our method uses a pre-trained network to extract multi-scale semantic features as reconstruction targets. Given the multiscale nature of industrial defects, we incorporate a training strategy involving random positional and quantitative masking. Moreover, we propose an innovative adaptive mask generator capable of generating adaptive masks that effectively mask anomalous regions while preserving normal regions. In this manner, the model can leverage the visible normal global contextual information to restore the masked anomalous regions, thereby effectively suppressing the reconstruction of defects. Extensive experimental results on the MVTec AD and BTAD industrial datasets validate the effectiveness of the proposed method. Additionally, AMI-Net exhibits exceptional real-time performance, striking a favorable balance between detection accuracy and speed, rendering it highly suitable for industrial applications. Code is available at: https://github.com/luow23/AMI-Net
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