LOCO: Logistics Objects in Context

Machine perception is a key challenge towards autonomous systems. Especially in the field of computer vision, numerous novel approaches have been introduced in recent years. This trend is based on the availability of public datasets. Logistics is one domain that could benefit from such innovations....

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Bibliographic Details
Published in2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) pp. 612 - 617
Main Authors Mayershofer, Christopher, Holm, Dimitrij-Marian, Molter, Benjamin, Fottner, Johannes
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2020
Subjects
Annotations
Data acquisition
Dataset
Logistics
Object detection
Pallets
Perception
Technological innovation
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DOI10.1109/ICMLA51294.2020.00102

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Summary:Machine perception is a key challenge towards autonomous systems. Especially in the field of computer vision, numerous novel approaches have been introduced in recent years. This trend is based on the availability of public datasets. Logistics is one domain that could benefit from such innovations. Yet, there are no public datasets available. Accordingly, we create the first public dataset for scene understanding in logistics. The Logistics Objects in COntext (LOCO) dataset contains 39,101 images. In its first release there are 5,593 bounding-box annotated images. In total 151,428 instances of pallets, small load carriers, stillages, forklifts and pallet trucks were annotated. We also present and discuss our data acquisition approach which features enhanced privacy protection for workers. Finally, we provide an in-depth analysis of LOCO, compare it to other datasets (i.e. OpenImages and MS COCO) and show that it has far more annotations per image and also a considerably smaller annotation size. The dataset and future extensions will be available on our website (https://github.com/tum-fml/loco).
DOI:10.1109/ICMLA51294.2020.00102