构建赛车场景检测数据集，解决高速模糊与无车道线难题
A Racing Dataset and Baseline Model for Track Detection in Autonomous Racing
- 用多相机采集真实赛车数据，标注赛道位置
- 提出RaceGAN模型，在模糊与无标记场景下表现更优
- 适合自动驾驶赛车与视觉感知研究者参考
赛车领域研究的一大挑战是缺乏公开的原始图像数据集及其标注。本文提出RoRaTrack数据集，包含在印第安纳州赛车场由Dallara AV-21采集的多摄像头图像数据，合作方为印第安纳自动驾驶挑战赛（IAC）。该数据集解决了高速运动导致的图像模糊、摄像头色彩反转及赛道无车道线等常见问题。为此，我们提出基于生成对抗网络（GAN）的RaceGAN基准模型，有效应对上述挑战。实验表明，该模型在赛道检测任务中优于现有最先进机器学习方法。相关数据与代码已开源：https://github.com/ghosh64/RaceGAN。
原文摘要 · Abstract (English)
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 https://github.com/ghosh64/RaceGAN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。