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 atthis https URL.
View on arXiv@article{vödisch2025_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 }
}