Deep Learning Strategies for Industrial Surface Defect Detection Systems

TL;DR

This paper explores deep learning approaches for industrial surface defect detection, addressing challenges like limited data and rare defects, and provides practical guidelines validated through a real-world case study.

Contribution

It systematically identifies challenges and proposes strategies for applying deep learning to industrial defect detection, supported by an experimental case study.

Findings

Strategies effectively address data scarcity and defect rarity.

Guidelines improve defect detection accuracy in industrial settings.

Case study validates practical applicability of proposed methods.

Abstract

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.