Open Set Industrial Surface Defect Recognition With High Frequency Feature Enhancement and Class Mutual-Information Constraint

Defect detection in multimedia data plays a pivotal role in industrial manufacturing. However, existing methods are primarily designed for closed-world scenarios and can only identify defect classes in the training data, limiting their ability to effectively detect unknown class defects that arise during production. To address this critical limitation, we propose a novel approach by introducing industrial defect open set recognition (IDOSR), which overcomes the challenge of recognizing unknown defect classes. Furthermore, to tackle the issues of limited training samples and subtle inter-class differences in IDOSR, we present a high-frequency feature enhancement open set recognition (HFFE-OSR) method. Specifically, HFFE-OSR employs a high-frequency structural feature fusion enhancement strategy to meticulously extract and fuse defect-related high-frequency structural features. This enables the network to comprehensively learn defect target representations even under limited training samples, resulting in robust feature extraction for known classes, thereby improving the discriminability between known and unknown classes and addressing the difficulty of distinguishing between them. Additionally, a class mutual information constraint strategy is introduced to measure and reduce the mutual information among defect features from different classes. This ensures the independence of defect features across known classes, further enhancing their discriminability and significantly improving recognition performance for known classes. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art OSR methods on ID-OSD and MVTec datasets, achieving improvements of at least 7% in accuracy (ACC), 22% in F1 score, and 10% in AUROC, highlighting the effectiveness of our approach in industrial defect detection.