Unsupervised industrial anomaly detection (UAD) is essential for modern manufacturing inspection, where defect samples are scarce and reliable detection is required. In this paper, we propose HLGFA, a high-low resolution guided feature alignment framework that learns normality by modeling cross-resolution feature consistency between high-resolution and low-resolution representations of normal samples, instead of relying on pixel-level reconstruction. Dual-resolution inputs are processed by a shared frozen backbone to extract multi-level features, and high-resolution representations are decomposed into structure and detail priors to guide the refinement of low-resolution features through conditional modulation and gated residual correction. During inference, anomalies are naturally identified as regions where cross-resolution alignment breaks down. In addition, a noise-aware data augmentation strategy is introduced to suppress nuisance-induced responses commonly observed in industrial environments. Extensive experiments on standard benchmarks demonstrate the effectiveness of HLGFA, achieving 97.9% pixel-level AUROC and 97.5% image-level AUROC on the MVTec AD dataset, outperforming representative reconstruction-based and feature-based methods.

翻译：无监督工业异常检测（UAD）在现代制造业质检中至关重要，其面临缺陷样本稀缺且需实现可靠检测的挑战。本文提出HLGFA——一种高-低分辨率引导的特征对齐框架，该方法通过建模正常样本的高分辨率与低分辨率表征间的跨分辨率特征一致性来学习正常模式，而非依赖像素级重建。双分辨率输入经共享冻结主干网络处理以提取多层级特征，其中高分辨率表征被解耦为结构先验与细节先验，通过条件调制与门控残差校正引导低分辨率特征的精细化。在推理阶段，异常区域可自然表现为跨分辨率对齐失效的区域。此外，本文引入噪声感知数据增强策略，以抑制工业环境中常见的干扰噪声响应。在标准基准测试上的大量实验验证了HLGFA的有效性，其在MVTec AD数据集上达到97.9%的像素级AUROC与97.5%的图像级AUROC，性能优于代表性的基于重建与基于特征的方法。