  While common image object detection tasks focus on bounding boxes or
segmentation masks as object representations, we propose a novel method, named
TetraPackNet, using fourcorner based object representations. TetraPackNet is
inspired by and based on CornerNet and uses similar base algorithms and ideas.
It is designated for applications were the high-accuracy detection of regularly
shaped objects is crucial, which is the case in the logistics use-case of
packaging structure recognition. We evaluate our model on our specific
real-world dataset for this use-case. Baselined against a previous solution,
consisting of a a Mask R-CNN model and suitable post-processing steps,
TetraPackNet achieves superior results (6% higher in accuracy) in the
application of four-corner based transport unit side detection.
