Year: 2022
Type: article
Abstract: This article presents the Formula Student Objects in Context (FSOCO) dataset, a collaborative dataset for vision-based cone detection systems in Formula Student Driverless (FSD) 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 the prediction results of a network trained on FSOCO and its unregulated predecessor. The FSOCO dataset can be found at fsoco-dataset.com.
Authors: Niclas Vödisch, David Dodel, Michael Schötz
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4205907166