A Self-Supervised Few-Shot Detection Method for Magnetic Tile Defects Detection

Current Defect detection methods have made significant progress on ideal datasets that typically contain a large number of defect samples. This enables the traditional defect detection methods to achieve great detection performance. However, in practical applications, the training samples obtained are often highly imbalanced, with the majority being non-defective samples and only a few being defective samples. It will generally lead to performance degradation of traditional methods if using such imbalanced samples for training. To address this issue, we propose a few-shot defect detection method based on self-supervised learning. Specifically, we propose to use a transfer learning strategy to transfer from traditional full-shot learning to few-shot learning. Next, we propose to process the training data in a self-supervised manner. As a result, the proposed method is enabled to achieve satisfactory detection performance on the industrial magnetic tile defect dataset. Experimental results verify the effectiveness of our proposed method.