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

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.

An Image Processing Pipeline for Automated Packaging Structure Recognition

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.

Fully-Automated Packaging Structure Recognition in Logistics Environments

Within a logistics supply chain, a large variety of transported goods need to be handled, recognized and checked at many different network points. Often, huge manual effort is involved in recognizing or verifying packet identity or packaging structure, for instance to check the delivery for completeness. We propose a method for complete automation of packaging structure recognition: Based on a single image, one or multiple transport units are localized and, for each of these transport units, the characteristics, the total number and the arrangement of its packaging units is recognized. Our algorithm is based on deep learning models, more precisely convolutional neural networks for instance segmentation in images, as well as computer vision methods and heuristic components. We use a custom data set of realistic logistics images for training and evaluation of our method. We show that the solution is capable of correctly recognizing the packaging structure in approximately 85% of our test cases, and even more (91%) when focusing on most common package types.

Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving

Two important requirements when aggregating the preferences of multiple agents are that the outcome should be economically efficient and the aggregation mechanism should not be manipulable. In this paper, we provide a computer-aided proof of a sweeping impossibility using these two conditions for randomized aggregation mechanisms. More precisely, we show that every efficient aggregation mechanism can be manipulated for all expected utility representations of the agents' preferences. This settles an open problem and strengthens a number of existing theorems, including statements that were shown within the special domain of assignment. Our proof is obtained by formulating the claim as a satisfiability problem over predicates from real-valued arithmetic, which is then checked using an SMT (satisfiability modulo theories) solver. In order to verify the correctness of the result, a minimal unsatisfiable set of constraints returned by the SMT solver was translated back into a proof in higher-order logic, which was automatically verified by an interactive theorem prover. To the best of our knowledge, this is the first application of SMT solvers in computational social choice.