  Within a logistics supply chain, a large variety of transported goods need to
be handled, recognized and checked at many different network points. Often,
huge manual effort is involved in recognizing or verifying packet identity or
packaging structure, for instance to check the delivery for completeness. We
propose a method for complete automation of packaging structure recognition:
Based on a single image, one or multiple transport units are localized and, for
each of these transport units, the characteristics, the total number and the
arrangement of its packaging units is recognized. Our algorithm is based on
deep learning models, more precisely convolutional neural networks for instance
segmentation in images, as well as computer vision methods and heuristic
components. We use a custom data set of realistic logistics images for training
and evaluation of our method. We show that the solution is capable of correctly
recognizing the packaging structure in approximately 85% of our test cases, and
even more (91%) when focusing on most common package types.
