Abstract
One of the most promising use-cases for machine learning in industrial manufacturing is the early detection of defective products using a quality control system. Such a system can save costs and reduces human errors due to the monotonous nature of visual inspections. Today, a rich body of research exists which employs machine learning methods to identify rare defective products in unbalanced visual quality control datasets. These methods typically rely on two components: A visual backbone to capture the features of the input image and an anomaly detection algorithm that decides if these features are within an expected distribution. With the rise of transformer architecture as visual backbones of choice, there exists now a great variety of different combinations of these two components, ranging all along the trade-off between detection quality and inference time. Facing this variety, practitioners in the field often have to spend a considerable amount of time on researching the right combination for their use-case at hand. Our contribution is to help practitioners with this choice by reviewing and evaluating current vision transformer models together with anomaly detection methods. For this, we chose SotA models of both disciplines, combine and evaluate them towards the goal of having small, fast and efficient anomaly detection models suitable for industrial manufacturing. We evaluate the results on the well-known MVTecAD and BTAD datasets and propose considerations for using a quality control system in practice.
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Acknowledgements
Christoph Hönes has received funding from SAP SE. Christoph Hönes and Miriam Alber were employed by esentri AG who also provided computational resources.
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Alber, M., Hönes, C., Baier, P. (2024). Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing. In: Bifet, A., Krilavičius, T., Miliou, I., Nowaczyk, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14950. Springer, Cham. https://doi.org/10.1007/978-3-031-70381-2_8
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