Mixed supervision for surface-defect detection: from weakly to fully supervised learning
By Jakob Bozic, Domen Tabernik, Danijel Skocaj. Computers in industry (Print), 2021
381 citations recorded by the source corpus.
Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data needed for learning, often requiring high-precision labels, many industrial problems cannot be easily solved, or the cost of the solutions would significantly increase due to the annotation requirements. In this work, we relax heavy requirements of fully supervised learning methods and reduce the need for highly detailed annotations. By proposing a deep-learning architecture, we explore the use of annotations of different details ranging from weak (image-level) labels through mixed supervision to full (pixel-level) annotations on the task of surface-defect detection. The proposed end-to-end architecture is composed of two sub-networks yielding defect segmentation and classification results. The proposed method is evaluated on several datasets for industrial quality inspection: KolektorSDD, DAGM and Severstal Steel Defect. We also present a new dataset termed KolektorSDD2 with over 3000 images containing several types of defects, obtained while addressing a real-world industrial problem. We demonstrate state-of-the-art results on all four datasets. The proposed method outperforms all related approaches in fully supervised settings and also outperforms weakly-supervised methods when only image-level labels are available. We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.
Subfields: Civil and Structural Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/7eb24c2109f75c614cc7aa4c1cac8b643c05e70c
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In Civil and Structural Engineering
In Industrial and Manufacturing Engineering
In Mechanical Engineering
Citations in the corpus
- Deep Industrial Image Anomaly Detection: A Survey (2023, background): “[113] examine the influence of image-level supervision information, mixed supervision information, and pixel-level supervision information on surface defect detection tasks within the same deep learning framework.”