A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC). RoRaTrack addresses common problems such as blurriness due to high speed, color inversion from the camera, and absence of lane markings on the track. Consequently, we propose RaceGAN, a baseline model based on a Generative Adversarial Network (GAN) that effectively addresses these challenges. The proposed model demonstrates superior performance compared to current state-of-the-art machine learning models in track detection. The dataset and code for this work are available at github.com/RaceGAN.

本研究针对赛车相关研究中缺乏公开可用数据集的问题，提出了RoRaTrack，一个包含多摄像头图像数据及相应注释的新数据集，专注于轨道检测。通过生成对抗网络（GAN）模型RaceGAN，我们有效应对高速模糊、相机色彩反转和轨道缺失标记等挑战，并在轨道检测性能上超越现有的先进机器学习模型，展现出其显著的应用潜力。

自动赛车轨道检测的数据集和基准模型