  The benefits of deep neural networks (DNNs) have become of interest for
safety critical applications like medical ones or automated driving. Here,
however, quantitative insights into the DNN inner representations are
mandatory. One approach to this is concept analysis, which aims to establish a
mapping between the internal representation of a DNN and intuitive semantic
concepts. Such can be sub-objects like human body parts that are valuable for
validation of pedestrian detection. To our knowledge, concept analysis has not
yet been applied to large object detectors, specifically not for sub-parts.
Therefore, this work first suggests a substantially improved version of the
Net2Vec approach (arXiv:1801.03454) for post-hoc segmentation of sub-objects.
Its practical applicability is then demonstrated on a new concept dataset by
two exemplary assessments of three standard networks, including the larger Mask
R-CNN model (arXiv:1703.06870): (1) the consistency of body part similarity,
and (2) the invariance of internal representations of body parts with respect
to the size in pixels of the depicted person. The findings show that the
representation of body parts is mostly size invariant, which may suggest an
early intelligent fusion of information in different size categories.
