UAD-X: A Universal Anomaly Detection Framework for X-Ray Inspection of Electronic Components

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Abstract
Anomaly detection in X-ray images of electronic components plays a vital role in quality control for advanced manufacturing. During the industrial production of electronic components, internal microdefects often arise due to material fatigue or process variations. Automatically identifying these defective regions remains a significant and challenging task in the field. However, existing methods typically rely on prior knowledge of normal categories and often suffer from limited generalization across diverse component types. To address these limitations, we propose UAD-X, a universal anomaly detection framework tailored for X-ray inspection of electronic components. Our method leverages a pretrained vision Transformer encoder (DINOv2) to extract robust image representations and introduces a normal reference representation (NRR) extractor that dynamically learns intrinsic normal patterns from a single image. A bottleneck module is employed to compress semantic information, and a reference-guided decoder reconstructs the normal regions, thereby suppressing anomalous features effectively. To enhance the detection of subtle defects, we further design an entropy-aware mining loss (EAML), which adaptively amplifies training signals based on the regional uncertainty. Extensive experiments conducted on our curated X-ray dataset of electronic components and the MVTec AD benchmark demonstrate that our approach significantly outperforms state-of-the-art (SOTA) methods in both pixel-level localization and image-level classification tasks. Specifically, on our electronic component X-ray dataset, the proposed method achieves an image-level area under the receiver operating characteristic (AUROC) of 92.5%, a pixel-level AUROC of 98.0%, and a pixel-level area under the per-region overlap (AUPRO) of 96.3%. Moreover, it exhibits strong cross-category generalization, achieving high performance without requiring retraining on new categories.
Funding Information
  • U.S. Department of Commerce (123456)

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