基于OOD评分的工业缺陷增强数据筛选研究

Study on Industrial Defect Augmentation Data Filtering Based on OOD Scores

在基于深度学习的工业缺陷检测中,数据增强能在一定程度上缓解部分缺陷数据缺乏的窘境,但如何从大量增强数据中筛选出有效的增强数据,提升工业检测模型的性能,目前尚未有相关研究。针对这一问题,进行了基于分布外检测(Out-of-Distribution Detection,OOD)评分的工业缺陷增强数据筛选研究。首先使用pix2pix网络生成工业增强数据,接着采用基于深度集成的OOD评分方法获得OOD评分,并利用该评分对增强数据进行分组;然后通过降维投影视图对增强数据分布进行分组观察;最后使用目标检测算法对增强数据进行分组缺陷检测,根据目标检测模型的精度增益探索分布外程度对增强数据质量的影响。实验结果表明,OOD评分较高的工业缺陷增强数据与训练数据分布差异较大,将这部分增强数据用于训练集的数据扩充能够提高模型的泛化性,可以更有效地提升目标检测算法的检测精度。

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.