Open-IndDet: Advancing Open-Set Industrial Surface Defect Detection via Robust Class-Unique Feature Representation

Industrial defect detection is crucial in the field of industrial production. Recently, industrial defect detection methods based deep learning have been proven to effectively address most industrial defect detection tasks. However, these methods are unable to detect and identify defects of both known and new categories simultaneously. In industrial settings, the emergence of new types of defects is a common occurrence, rendering traditional closed set defect detection approaches inadequate for identifying these new defects. Therefore, to tackle this limitation, we introduce the concept of open set defect detection in industrial contexts, specifically formulated to recognize new types of defects. Furthermore, to address the challenges of limited training samples and small differences between defect classes in industrial defect open set detection, we propose a novel two-stage end-to-end methodology, termed Open Set Industrial Defect Detection (Open-IndDet). The core idea of Open-IndDet is to increase the discrimination boundary between known and new classes of defects by enhancing defect structural features and extracting class-unique robust features. Specifically, in the Open-IndDet, a class-unique robust feature extraction open set recognition strategy is proposed. This strategy fuses the structural features of defects in the defect confidence image with the defect features in the original image, thereby enhancing the expression of the structural features of defects and achieving full learning of defect features under limited training samples. In addition, this strategy extracts robust class-unique features by constraining the uniqueness of features within defect classes and enhancing the entropy of inter-class distribution differences. Such a strategy can increase the differences between the features of defects of different classes, achieving accurate classification of known class and identification of unknown class. The experimental results show that in our constructed standard open set detection dataset ID-OSD and public dataset MVTec, the proposed Open-IndDet achieved the best performance compared to existing advanced open set recognition methods.