Unsupervised industrial image ensemble anomaly detection based on object pseudo-anomaly generation and normal image feature combination enhancement

Haoyuan, Shen; Wei, Baolei; Ma, Yizhong; Gu, Xiaoyu

doi:10.1016/j.cie.2023.109337

Cited by 4 publications

(2 citation statements)

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“…Shen et al [25] address the challenge of anomaly detection in industrial image data when anomaly samples are scarce. Their unsupervised ensemble method generates highquality pseudo-anomaly images for training and demonstrates performance improvements on real datasets.…”

Section: Unsupervised Anomaly Detectionmentioning

confidence: 99%

“…The recent contributions underscore the array of approaches and techniques deployed in the realm of unsupervised visual anomaly detection for industrial applications, laying the foundation for more robust and efficient anomaly detection solutions across diverse industrial settings [24][25][26][27][28][29][30][31][32][33][34][35][36][37]. It is essential to emphasize that, distinct from unsupervised learning in other vision tasks, unsupervised anomaly detection tasks leverage anomaly-free images for training, leading to a paradigm where models inherently operate under the out-of-distribution concept.…”

Section: Unsupervised Anomaly Detectionmentioning

confidence: 99%

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Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification

Qiu,

Shi,

et al. 2023

Applied Sciences

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Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%.

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Section: Unsupervised Anomaly Detectionmentioning

confidence: 99%