论文标题
FSOCO：上下文数据集中的配方奶学生对象
FSOCO: The Formula Student Objects in Context Dataset
论文作者
论文摘要
本文介绍了FSOCO数据集，这是一种用于公式学生无人驾驶竞赛中的基于视觉锥检测系统的协作数据集。它包含人类注释的地面真理标签，用于边界框和实例分段面具。 FSOCO的数据买入理念要求学生团队首先为数据库做出贡献，然后才能获得访问确保持续增长。通过为精致的原始图像选择提供明确的标签指南和工具，可以确保新的注释可满足所需的质量。通过比较对FSOCO及其不受监管的前身训练的网络的预测结果来显示该方法的有效性。可以在https://fsoco.github.io/fsoco-dataset/上找到FSOCO数据集。
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/.
