This paper presents the FSOCO dataset, a collaborative dataset for vision-based cone detection systems in Formula Student Driverless competitions. It contains human annotated ground truth labels for both bounding boxes and instance-wise segmentation masks. The data buy-in philosophy of FSOCO asks student teams to contribute to the database first before being granted access ensuring continuous growth. By providing clear labeling guidelines and tools for a sophisticated raw image selection, new annotations are guaranteed to meet the desired quality. The effectiveness of the approach is shown by comparing prediction results of a network trained on FSOCO and its unregulated predecessor. The FSOCO dataset can be found at https://fsoco.github.io/fsoco-dataset/.
@article{arxiv.2012.07139,
title = {FSOCO: The Formula Student Objects in Context Dataset},
author = {Niclas Vödisch and David Dodel and Michael Schötz},
journal= {arXiv preprint arXiv:2012.07139},
year = {2025}
}