FSOCO: The Formula Student Objects in Context Dataset

TL;DR

The paper introduces FSOCO, a comprehensive dataset for vision-based cone detection in Formula Student Driverless competitions, emphasizing collaborative growth, high-quality annotations, and improved detection performance.

Contribution

It presents a new, collaboratively built dataset with detailed annotations and quality control for cone detection in autonomous racing, enhancing research and development in this domain.

Findings

Network trained on FSOCO outperforms models trained on unregulated data.

The dataset's quality guidelines ensure consistent and accurate annotations.

Collaborative data collection fosters continuous dataset growth.

Abstract

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/.