# FSOCO: The Formula Student Objects in Context Dataset

arXiv: https://arxiv.org/abs/2012.07139 · HTML: https://mlsanity.com/paper/2012.07139/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds](https://mlsanity.com/paper/2310.18112/) (2023)
- [A Modular Architecture Design for Autonomous Driving Racing in Controlled Environments](https://mlsanity.com/paper/2512.03886/) (2025)
- [UNet-Based Keypoint Regression for 3D Cone Localization in Autonomous Racing](https://mlsanity.com/paper/2602.21904/) (2026)

## Abstract (arXiv)

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/.