Improving Unsupervised Ultrasonic Image Anomaly Detection via Frequency-Spatial Feature Filtering and Gaussian Mixture Modeling.

Abstract

Ultrasonic image anomaly detection faces significant challenges due to limited labeled data, strong structural and random noise, and highly diverse defect manifestations. To overcome these obstacles, we introduce UltraChip, a new large-scale C-scan benchmark containing about 8,000 real-world images from various chip packaging types, each meticulously annotated with pixel-level masks for cracks, holes, and layers. Building on this resource, we present FSGM-Net, a fully unsupervised framework tailored for anomaly detection. FSGM-Net leverages an adaptive Frequency-Spatial feature filtering mechanism: a learnable FFT-Spatial patch filter first suppresses noise and dynamically assigns normality weights to Vision Transformer (ViT) patch features. Subsequently, an Adaptive Gaussian Mixture Model (Ada-GMM) captures the distribution of normal features and guides a deep-shallow multi-scale interaction decoder for accurate, pixel-level anomaly inference. In addition, we propose a filter loss that enforces encoder-filter consistency and entropy-based sparse gating, together with a distributional loss that encourages both feature reconstruction and confident Gaussian mixture modeling. Extensive experiments demonstrate that FSGM-Net not only achieves state-of-the-art results on UltraChip but also exhibits superior cross-domain generalization to MVTec-AD and VisA, while supporting real-time inference on a single GPU. Together, the dataset and framework advance robust, annotation-free ultrasonic NDT in practical applications. The UltraChip dataset can be obtained via https://iiplab.net/ultrachip/.