  Convolutional Neural Networks (CNN) have become a common choice for
industrial quality control, as well as other critical applications in the
Industry 4.0. When these CNNs behave in ways unexpected to human users or
developers, severe consequences can arise, such as economic losses or an
increased risk to human life. Concept extraction techniques can be applied to
increase the reliability and transparency of CNNs through generating global
explanations for trained neural network models. The decisive features of image
datasets in quality control often depend on the feature's scale; for example,
the size of a hole or an edge. However, existing concept extraction methods do
not correctly represent scale, which leads to problems interpreting these
models as we show herein. To address this issue, we introduce the
Scale-Preserving Automatic Concept Extraction (SPACE) algorithm, as a
state-of-the-art alternative concept extraction technique for CNNs, focused on
industrial applications. SPACE is specifically designed to overcome the
aforementioned problems by avoiding scale changes throughout the concept
extraction process. SPACE proposes an approach based on square slices of input
images, which are selected and then tiled before being clustered into concepts.
Our method provides explanations of the models' decision-making process in the
form of human-understandable concepts. We evaluate SPACE on three image
classification datasets in the context of industrial quality control. Through
experimental results, we illustrate how SPACE outperforms other methods and
provides actionable insights on the decision mechanisms of CNNs. Finally, code
for the implementation of SPACE is provided.
