  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.
