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.
While common image object detection tasks focus on bounding boxes or segmentation masks as object representations, we consider the problem of finding objects based on four arbitrary vertices. We propose a novel model , named TetraPackNet, to tackle this problem . TetraPackNet is based on CornerNet and uses similar algorithms and ideas. It is designated for applications requiring high-accuracy detection of regularly shaped objects , 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 Mask R-CNN model and suitable post-processing steps, TetraPackNet achieves superior results ( 9 \% higher in accuracy) in the sub-task of four-corner based transport unit side detection.
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consider the problem of finding objects based on four arbitrary vertices. We
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using fourcorner based object representations
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is crucial
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