FAD-RNet: A Reverse Distillation Network with Frequency-Decoupled Feature Fusion for Unsupervised Fabric Defect Localization

Unsupervised anomaly detection in industrial fabric inspection remains a formidable challenge due to the complexity of background textures and the subtle, irregular nature of real-world defects. Although the teacher-student distillation paradigm has demonstrated promising performance without reliance on anomalous data, existing methods still struggle in the presence of complex textures, largely due to limited semantic guidance, insufficient frequency modeling, and inadequate multi-scale representation. To address these limitations, we propose a novel reverse distillation framework tailored for fabric defect detection. The core of our method is the frequency decoupling Feature fusion module (FDFM), which achieves frequency domain alignment between teacher and student features through spatially adaptive and learnable filter banks, namely the adaptive high-pass filter (AHPF) and the adaptive low-pass filter (ALPF). Specifically: (1) the high-frequency pathway employs deconvolutional residual enhancement to emphasize boundary details; (2) the low-frequency pathway leverages the CARAFE operator to Handle these normal fluctuations to prevent the model from mistakenly identifying background changes as abnormal areas. This design not only maintains a lightweight architecture but also significantly improves sensitivity to fine-grained anomalies. Furthermore, we introduce a cross-layer residual alignment mechanism that guides the student network in reconstructing deep semantic representations from the teacher-student feature pairs. To balance detection accuracy and deployment efficiency, we develop two model variants: a high-capacity version optimized for precision, and a lightweight version tailored for real-time industrial applications. Compared with other methods from recent years, the experimental results of FAD-RNet validate its superiority in relevant metrics. It should be noted that this study is conducted based on the data organization and processing protocol of the ZJU-Leaper dataset, which may introduce certain dataset-specific characteristics.