摘要 : Deep learning methods have shown promising achievements, yet require a substantial quantity of training data. In industrial manufacturing scenarios, the training samples for certain defect categories might be small. Such a few-sho... 展开 Deep learning methods have shown promising achievements, yet require a substantial quantity of training data. In industrial manufacturing scenarios, the training samples for certain defect categories might be small. Such a few-shot learning problem severely obstacles the application of deep learning. Moreover, challenges such as small targets that are scarcely distinguishable from the background, coupled with defect category confusion, further complicate defect detection. To address these issues, this study proposes a novel approach called the object disentanglement and contrastive learning (ODCL) model. First, we introduce a significant region disentanglement module to decouple the foreground from the background. This is the pioneering application of disentanglement in few-shot industrial defect detection. Subsequently, we advance a supervised contrastive learning (SCL) model to alleviate defect category confusion. At last, we resolve the few-shot learning through a two-stage fine-tuning (TFA) method. Experimental results on three industrial datasets demonstrate that the ODCL achieves state-of-the-art results in various few-shot scenarios. Code and data are available at https://github.com/LiBiGo/ODCL. 收起