Improving Industrial Anomaly Detection with RealNet: A Unified AI Framework for Realistic Anomaly Synthesis and Efficient Feature Reconstruction

In industrial image anomaly detection, self-supervised feature reconstruction methods are promising, but still face challenges such as generating realistic and diverse anomaly samples while mitigating feature redundancy and pre-training bias. Synthetic anomalies lack diversity and realism, making model generalization difficult. Meanwhile, feature reconstruction-based detection, although simple, needs to improve under high computational demands and requires more effective feature selection. Recent studies emphasize the importance of feature selection, urging a unified approach to advance anomaly detection, which is crucial in industrial quality control and safety monitoring.

Researchers from the College of Information and Engineering at Capital Normal University and the School of artificial intelligence at Beijing University of Posts and Telecommunications have developed RealNet, a feature reconstruction framework incorporating force-controllable diffusion anomaly synthesis ( SDAS) that generates diverse and realistic aligned anomalies. with natural distributions, anomaly-aware feature selection (AFS) and reconstruction residual selection (RRS). RealNet improves anomaly detection by efficiently using pre-trained CNN features, reducing redundancy and bias. It introduces SDAS for realistic anomaly synthesis, AFS for feature selection, and RRS for adaptive residual selection. RealNet outperforms existing methods on benchmark datasets and introduces the Synthetic Industrial Anomalies (SIA) dataset for anomaly synthesis, facilitating self-supervised detection methods.

Unsupervised anomaly detection methods rely solely on normal data for training and are divided into four categories: reconstruction-based self-supervised learning, deep feature embedding, and one-class classification. The study focuses on self-supervised learning and reconstruction methods, which are crucial to the RealNet framework. While reconstruction methods struggle to reconstruct anomalies effectively, recent studies emphasize anomaly detection by reconstructing pre-trained features. However, challenges remain in feature redundancy and selection between different categories of anomalies. In contrast, self-supervised methods such as SDAS enable realistic anomaly synthesis without labeled data, offering control over anomaly intensities using only normal images.

RealNet is a framework for anomaly detection consisting of SDAS, AFS, and RRS. SDAS generates anomalous images with different intensities, imitating real anomalies. AFS selects pre-trained discriminative features, reducing redundancy and controlling costs. RRS adaptively selects discriminative residuals for anomaly identification. RealNet outperforms existing methods on benchmark data sets and introduces SIA for anomaly synthesis. The evaluation includes FID metrics and comparisons with other methods such as RDR and RLPR.

RealNet outperforms state-of-the-art Image AU-ROC and Pixel AUROC methods on four benchmark data sets. The RealNet framework demonstrates significant improvements in both Image AU-ROC and Pixel AUROC compared to current state-of-the-art methods. RealNet achieves substantial performance improvement compared to previous reconstruction-based methods. The results show that RealNet performs better than alternative methods such as PatchCore, SimpleNet and FastFlow. Evaluation of the quality of anomaly images generated by RealNet using FID (Frechet Onset Distance) shows that the synthetic anomaly images are close to the distribution of real anomaly images.

In conclusion, RealNet is a state-of-the-art framework for self-supervised anomaly detection that comprises three key elements: SDAS, AFS, and RRS. Together, these components enable RealNet to leverage large-scale pretrained models effectively for anomaly detection while ensuring computational efficiency. It offers a versatile platform for future anomaly detection research, particularly focusing on pre-trained feature reconstruction techniques. Extensive experiments demonstrate RealNet's ability to address various real-world anomaly detection scenarios proficiently and effectively.

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