Spacecraft deployed in outer space are routinely subjected to various forms of damage due to exposure to hazardous environments. In addition, there are significant risks to the subsequent process of in-space repairs through human extravehicular activity or robotic manipulation, incurring substantial operational costs. Recent developments in image segmentation could enable the development of reliable and cost-effective autonomous inspection systems. While these models often require large amounts of training data to achieve satisfactory results, publicly available annotated spacecraft segmentation data are very scarce. Here, we present a new dataset of nearly 64k annotated spacecraft images that was created using real spacecraft models, superimposed on a mixture of real and synthetic backgrounds generated using NASA's TTALOS pipeline. To mimic camera distortions and noise in real-world image acquisition, we also added different types of noise and distortion to the images. Our dataset includes images with several real-world challenges, including noise, camera distortions, glare, varying lighting conditions, varying field of view, partial spacecraft visibility, brightly-lit city backgrounds, densely patterned and confounding backgrounds, aurora borealis, and a wide variety of spacecraft geometries. Finally, we finetuned YOLOv8 and YOLOv11 models for spacecraft segmentation to generate performance benchmarks for the dataset under well-defined hardware and inference time constraints to mimic real-world image segmentation challenges for real-time onboard applications in space on NASA's inspector spacecraft. The resulting models, when tested under these constraints, achieved a Dice score of 0.92, Hausdorff distance of 0.69, and an inference time of about 0.5 second. The dataset and models for performance benchmark are available at https://github.com/RiceD2KLab/SWiM.

翻译：部署于外层空间的航天器常因暴露于危险环境而遭受各种形式的损伤。此外，通过人类舱外活动或机器人操作进行在轨修复的后续过程存在显著风险，并产生高昂的操作成本。图像分割技术的最新进展为开发可靠且经济高效的自主检测系统提供了可能。尽管这些模型通常需要大量训练数据才能获得满意结果，但公开可用的标注航天器分割数据极为稀缺。本文提出一个包含近6.4万张标注航天器图像的新型数据集，该数据集使用真实航天器模型创建，并叠加于通过NASA TTALOS流程生成的实景与合成背景混合图像之上。为模拟真实图像采集中的相机畸变与噪声，我们还对图像添加了多种类型的噪声与畸变。本数据集涵盖具有多重现实挑战的图像，包括噪声、相机畸变、眩光、多变光照条件、可变视场角、航天器部分可见性、明亮城市背景、密集图案与混淆背景、极光现象以及多样化的航天器几何形态。最后，我们针对航天器分割任务对YOLOv8和YOLOv11模型进行微调，在明确定义的硬件与推理时间约束下生成数据集的性能基准，以模拟NASA巡检航天器星载实时应用中面临的真实图像分割挑战。在此约束条件下测试的最终模型取得了0.92的Dice分数、0.69的豪斯多夫距离以及约0.5秒的推理时间。性能基准所用数据集与模型已发布于https://github.com/RiceD2KLab/SWiM。