no code implementations • 11 Aug 2020 • Laura Dörr, Felix Brandt, Martin Pouls, Alexander Naumann
Within a logistics supply chain, a large variety of transported goods need to be handled, recognized and checked at many different network points.
no code implementations • 29 Sep 2020 • Laura Dörr, Felix Brandt, Martin Pouls, Alexander Naumann
Dispatching and receiving logistics goods, as well as transportation itself, involve a high amount of manual efforts.
no code implementations • 19 Apr 2021 • Laura Dörr, Felix Brandt, Alexander Naumann, Martin Pouls
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.
no code implementations • 30 Nov 2023 • Daniel Grimm, Maximilian Zipfl, Felix Hertlein, Alexander Naumann, Jürgen Lüttin, Steffen Thoma, Stefan Schmid, Lavdim Halilaj, Achim Rettinger, J. Marius Zöllner
Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between traffic agents, map context and traffic rules.
1 code implementation • 12 Apr 2023 • Alexander Naumann, Felix Hertlein, Laura Dörr, Steffen Thoma, Kai Furmans
Computer vision applications in transportation logistics and warehousing have a huge potential for process automation.
1 code implementation • 6 Nov 2023 • Alexander Naumann, Felix Hertlein, Laura Dörr, Kai Furmans
We propose a tampering detection pipeline that utilizes keypoint detection to identify the eight corner points of a parcel.
1 code implementation • 18 Apr 2023 • Alexander Naumann, Felix Hertlein, Laura Dörr, Kai Furmans
We work towards detecting mishandling of parcels by presenting a novel architecture called CubeRefine R-CNN, which combines estimating a 3D bounding box with an iterative mesh refinement.
3 code implementations • 18 Oct 2022 • Alexander Naumann, Felix Hertlein, Benchun Zhou, Laura Dörr, Kai Furmans
This approach of image scraping and selection relaxes the need for a real-world domain-specific dataset that must be either publicly available or created for this purpose.
1 code implementation • 28 Mar 2020 • Alexander Naumann, Laura Dörr, Niels Ole Salscheider, Kai Furmans
Our approach is motivated by logistics, where this assumption is valid and refined planes can be used to perform robust object detection without the need for supervised learning.