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While common image object detection tasks focus on bounding boxes orsegmentation masks as object representations, we consider the problem offinding objects based on four arbitrary vertices. We propose a novel model,named TetraPackNet, to tackle this problem. TetraPackNet is based on CornerNetand uses similar algorithms and ideas. It is designated for applicationsrequiring high-accuracy detection of regularly shaped objects, which is thecase in the logistics use-case of packaging structure recognition. We evaluateour model on our specific real-world dataset for this use-case. Baselinedagainst a previous solution, consisting of a Mask R-CNN model and suitablepost-processing steps, TetraPackNet achieves superior results (9% higher inaccuracy) in the sub-task of four-corner based transport unit side detection.

TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases