Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy

Recent anomaly detection methods achieve high performance on commonly used image and pixel-level metrics.However, due to the imbalance in the number of normal and abnormal pixels commonly encountered in anomaly detection problems, commonly adopted pixel-level performance metrics cannot effectively evaluate model performance.This paper proposes a novel approach for anomaly detection within the irregular texture domain, focusing on pixel-level accuracy metrics suitable for such imbalanced problems.The proposed Superpixelbased Coupled-hypersphere-based Feature Adaptation (Sp-CFA) method leverages the intermediate adaptive representation of superpixels to enable superior pixel-level anomaly detection performance.We demonstrate superior performance over the irregular texture classes within the MVTec AD benchmark dataset, KSDD2 dataset, and an X-ray dataset of manufactured fibrous products.