Visual-based defect detection is a crucial but challenging task in industrial quality control. Most mainstream methods rely on large amounts of existing or related domain data as auxiliary information. However, in actual industrial production, there are often multi-batch, low-volume manufacturing scenarios with rapidly changing task demands, making it difficult to obtain sufficient and diverse defect data. This paper proposes a parallel solution that uses a human-machine knowledge hybrid augmentation method to help the model extract unknown important features. Specifically, by incorporating experts' knowledge of abnormality to create data with rich features, positions, sizes, and backgrounds, we can quickly accumulate an amount of data from scratch and provide it to the model as prior knowledge for few-data learning. The proposed method was evaluated on the magnetic tile dataset and achieved F1-scores of 60.73%, 70.82%, 77.09%, and 82.81% when using 2, 5, 10, and 15 training images, respectively. Compared to the traditional augmentation method's F1-score of 64.59%, the proposed method achieved an 18.22% increase in the best result, demonstrating its feasibility and effectiveness in few-data industrial defect detection.

翻译：基于视觉的缺陷检测是工业质量控制中一项关键但具有挑战性的任务。大多数主流方法依赖大量现有或相关领域数据作为辅助信息。然而，在实际工业生产中，常存在多批次、小批量且任务需求快速变化的制造场景，导致难以获取充足且多样的缺陷数据。本文提出一种并行解决方案，采用人机知识混合增强方法帮助模型提取未知的重要特征。具体而言，通过融入专家对异常的知识，生成具有丰富特征、位置、尺寸和背景的数据，从而从零开始快速积累数据量，并将其作为先验知识提供给模型进行小样本学习。该方法在磁瓦数据集上进行了评估，当使用2、5、10和15张训练图像时，分别取得了60.73%、70.82%、77.09%和82.81%的F1分数。相比传统增强方法64.59%的F1分数，本方法的最佳结果提升了18.22%，证明了其在工业小样本缺陷检测中的可行性和有效性。