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.

Dispatching and receiving logistics goods, as well as transportation itself, involve a high amount of manual efforts. The transported goods, including their packaging and labeling, need to be double-checked, verified or recognized at many supply chain network points. These processes hold automation potentials, which we aim to exploit using computer vision techniques. More precisely, we propose a cognitive system for the fully automated recognition of packaging structures for standardized logistics shipments based on single RGB images. Our contribution contains descriptions of a suitable system design and its evaluation on relevant real-world data. Further, we discuss our algorithmic choices.