FSOCO: The Formula Student Objects in Context Dataset
This dataset addresses the problem of high-quality, standardized data for student teams developing vision systems in Formula Student Driverless competitions.
This paper introduces FSOCO, a new dataset for vision-based cone detection in Formula Student Driverless competitions, featuring human-annotated bounding boxes and instance-wise segmentation masks. The dataset's quality is maintained through clear labeling guidelines and sophisticated image selection, and its effectiveness is demonstrated by improved prediction results compared to an unregulated predecessor.
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/.