As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data. Despite the strong practical and commercial interest in autonomous landing systems in the aerospace field, there is a lack of open-source datasets of aerial images. To address this issue, we present a dataset-lard-of high-quality aerial images for the task of runway detection during approach and landing phases. Most of the dataset is composed of synthetic images but we also provide manually labelled images from real landing footages, to extend the detection task to a more realistic setting. In addition, we offer the generator which can produce such synthetic front-view images and enables automatic annotation of the runway corners through geometric transformations. This dataset paves the way for further research such as the analysis of dataset quality or the development of models to cope with the detection tasks. Find data, code and more up-to-date information at https://github.com/deel-ai/LARD
翻译:随着自主系统的不断发展,收集足够且具有代表性的现实世界数据是一个重要的挑战。尽管在航空航天领域中对自治着陆系统的实际和商业兴趣强烈,但缺乏空中图像的开源数据集。为了解决这个问题,我们提供了一个高质量的航拍图像数据集,LARD,用于着陆进近和着陆阶段的跑道探测任务。数据集的大部分是由合成图像组成的,但我们还提供了从实际着陆镜头手动标记的图像,以将探测任务扩展到更逼真的设置。此外,我们还提供了生成器,可以生成这样的合成前视图像,并通过几何变换自动注释跑道角落。这个数据集为进一步研究提供了先决条件,如数据集质量的分析或开发模型以应对探测任务。在https://github.com/deel-ai/LARD找到数据、代码和更多最新的信息。