  Deep learning methods have proven to outperform traditional computer vision
methods in various areas of image processing. However, the application of deep
learning in industrial surface defect detection systems is challenging due to
the insufficient amount of training data, the expensive data generation
process, the small size, and the rare occurrence of surface defects. From
literature and a polymer products manufacturing use case, we identify design
requirements which reflect the aforementioned challenges. Addressing these, we
conceptualize design principles and features informed by deep learning
research. Finally, we instantiate and evaluate the gained design knowledge in
the form of actionable guidelines and strategies based on an industrial surface
defect detection use case. This article, therefore, contributes to academia as
well as practice by (1) systematically identifying challenges for the
industrial application of deep learning-based surface defect detection, (2)
strategies to overcome these, and (3) an experimental case study assessing the
strategies' applicability and usefulness.
