Abstract: In deep learning-based industrial defect detection,data augmentation plays a crucial role in mitigating the scarcity of defect data.However,the effective selection of augmented data from a vast pool of candidates remains an unexplored area,hampering the performance enhancement of industrial detection models.To address this issue,this study focuses on the research of industrial defect augmentation data filtering based on out-of-distribution(OOD) scores.The proposed approach involves the generation of industrial enhancement data using the pix2pix network.Subsequently,OOD scores are computed using a deep ensemble-based scoring method,which facilitates the grouping of augmented data based on their OOD scores.Furthermore,the distribution of the augmented data is analyzed through dimensionality reduction and projection views.Finally,defect detection of the grouped augmented data is performed using object detection algorithms,while investigating the impact of the out-of-distribution degree on the quality of the augmented data through the accuracy gain of the object detection model.Experimental results demonstrate a substantial difference in the distribution between industrial defect augmented data with higher OOD scores and the training data.Incorporating this subset of augmented data for training data expansion enhances the generalization of the model and significantly improves the detection accuracy of the object detection algorithm.

Key words: Data augmentation, Defect detection, Out-of-distribution detection, Data visualization, Deep learning