  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.
