Enhanced Industrial Anomaly Detection via CutMask Data Augmentation: A Self-Supervised Approach

Deep learning techniques have revolutionized industrial anomaly detection; how-ever, their reliance on substantial labeled data poses challenges in scenarios where anomalous samples are scarce. This paper introduces a novel self-supervised anomaly detection framework, CutMask-based Anomaly Detection (CMAD), designed to detect anomalies using only normal samples. CMAD incorporates an improved data augmentation method, CutMask, which leverages prior knowledge of defect shapes to generate realistic simulated defect samples. Furthermore, it employs an enhanced Cyclical Adversarial Focal Loss function to improve sample discrimination and utilizes a lightweight ResNet-18 model for efficient defect detection. A Self-Supervised Predictive Convolutional Attentive Block (SSP-CAB) module is integrated to enhance feature modeling. Experimental results on the MVTecAD dataset and a practical presswork dataset demonstrate CMAD’s superior performance, achieving an AUC-ROC score of 97.8% on MVTecAD, out-performing existing methods. This framework offers a practical solution to the challenge of limited anomalous data in industrial settings.The code and dataset are available at: https://github.com/Jared-Yao/CutMask.