GASR-Net: Geometric Anomaly Synthesis and Task-Oriented Feature Reconstruction for Industrial Anomaly Detection

In real-world industrial manufacturing, automated visual inspection for surface defect detection is crucial for quality control but remains severely hindered by two critical challenges: the simplified assumption of isotropic anomaly synthesis that fails to capture complex physical defect shapes, and the over-smoothing effect in generative models that triggers unacceptable false alarms in textured backgrounds. To address these persistent industrial limitations, we propose a novel network, GASR-Net (Geometric Anomaly Synthesis and Task-Oriented Feature Reconstruction for unsupervised image anomaly detection and localization). Our architecture tackles the gap between synthesized noise and realistic flaw shapes by introducing a Geometric-Aware Deformable Noise Modulator (GAND-M). By utilizing baseline normal features to dynamically predict deformation offsets, the module warps isotropic Gaussian noise into structurally contiguous and geometric-diverse pseudo-anomaly flows. Simultaneously, to overcome the persistent issue of background over-smoothing and the resulting high false positive rates, we design a Task-Oriented Single-Step Feature Reconstructor based on a lightweight U-Net topology. Unlike conventional generative methods that perform blind standalone restoration, our constructor is driven by a joint feedback loop from the downstream discriminator, adaptively forcing the network to thoroughly eliminate abnormal artifacts while strictly preserving pixel-level background fidelity. Extensive experiments on two challenging industrial anomaly detection benchmarks, MVTec AD and VisA, demonstrate that GASR-Net achieves state-of-the-art performance in both detection and localization accuracy.