30万+图像构建机场滑行道数据集，助力自动驾驶飞行研究
AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations
- 收集墨尔本与格兰特-瓦尔卡里机场30万+图像，覆盖多样场景
- 支持滑行道分析算法训练与评估，推动航空自动驾驶发展
- 提供轮廓检测标注方法，适合交通感知与智能导航研究者
高质量数据集对安全关键型自主系统的研究与开发至关重要。本文提出AssistTaxi，一个用于跑道与滑行道分析的综合性新数据集，包含超过30万帧来自墨尔本（MLB）和格兰特-瓦尔卡里（X59）通用航空机场的多样化、精心采集的图像。该数据集有望推动自主运行技术的发展，使研究人员能够训练和评估高效安全滑行的算法。研究者可利用AssistTaxi进行算法基准测试、性能评估，并探索新型滑行道分析方法。此外，该数据集还可用于验证与优化现有算法，促进航空自主运行领域的创新。我们还提出一种基于轮廓的检测与线段提取技术，用于初步标注该数据集。
原文摘要 · Abstract (English)
The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. In this paper, we present AssistTaxi, a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing. Researchers can utilize AssistTaxi to benchmark their algorithms, assess performance, and explore novel approaches for runway and taxiway analysis. Addition-ally, the dataset serves as a valuable resource for validating and enhancing existing algorithms, facilitating innovation in autonomous operations for aviation. We also propose an initial approach to label the dataset using a contour based detection and line extraction technique.
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