Towards Graph Representation based Re-Identification of Chipwood Pallet Blocks

This contribution provides a novel graph representation-based approach for the re-identification of chipwood surface structures and, in the herein observed use case, the re-identification of Euro-pallets. For this purpose, we suggest, in contrast to common re-identification approaches, replacing the usual image representation with a highly compressed graph representation. This allows for the creation of an efficient algorithm while also providing robustness to environmental changes such as rotation and shearing. The resulting method, called IRAG (Image Representation through Anomaly Graphs), is a siamese graph neural network, that is applied on a previously published dataset consisting of images from 502 EPAL pallet blocks. The results of this approach lead to a rank-1 accuracy of 27% when re-identifying pallet blocks. Even though IRAG does not yet reach accuracy values that are comparable to state-of-the-art literature, it is however more efficient, concerning the handling and representation of data. In addition, the experiments in this work demonstrate that the re-identification accuracy of the model is not affected by rotation or shearing, demonstrating the model’s invariance to these environmental changes.