<b>NCSTP: </b><b>A Benchmark Dataset for Non-Cooperative Space Target Perception</b>

The automatic, accurate perception of targets in space is a crucial prerequisite for many aerospace missions, such as on-orbit maintenance and target monitoring. Therefore, research on perception technologies within images from spaceborne cameras, is of great significance. The recent, rapid development of deep learning has revealed its potential for application to space target perception. However, implementing deep learning models typically requires large-scale labeled datasets. In this study, we build a multitask benchmark space target dataset, NCSTP, to address the limitations of current datasets. First, we collect and modify various space target models for satellites, space debris, and space rocks. By importing them into a realistic space environment simulated by Blender, 200,000 images are generated with different target sizes, poses, lighting conditions, and backgrounds. Then, the data are annotated to ensure the dataset supports simultaneous space target detection, recognition and component segmentation. NCSTP has 10 fine-grained classes of satellites, 6 classes of space debris, and 4 classes of space rocks. All the data can be used for training space target detection and recognition models. We further annotate the body, solar panels, antennas, and observation payloads of each satellite for component segmentation. Finally, we test a series of state-of-the-art object detection and semantic segmentation models on the dataset to establish a benchmark.<br>2025.6.16: A smaller version NCSTP-10000 is available now

对空间目标开展自动、精准的感知，是在轨维护、目标监视等诸多航天任务的核心前提。因此，针对星载相机图像的空间目标感知技术研究具有重要意义。近年来深度学习的快速发展，展现了其在空间目标感知领域的应用潜力。然而，深度学习模型的落地通常依赖大规模带标注数据集。本研究针对现有数据集的局限性，构建了多任务基准空间目标数据集NCSTP。首先，我们收集并优化了适用于卫星、空间碎片和太空岩石的各类空间目标模型，将其导入Blender（Blender）构建的逼真太空环境中，生成了20万张涵盖不同目标尺寸、姿态、光照条件与背景的图像。随后，对数据进行多维度标注，使该数据集可同时支持空间目标检测、识别与部件分割三项任务。NCSTP包含10个细粒度卫星类别、6个空间碎片类别以及4个太空岩石类别，所有数据均可用于空间目标检测与识别模型的训练。我们进一步对每颗卫星的本体、太阳能帆板、天线与观测载荷进行标注，以支撑部件分割任务。最终，我们在该数据集上对一系列当前前沿的目标检测与语义分割模型开展测试，以此建立该领域的基准性能标杆。
2025年6月16日：现已推出精简版本NCSTP-10000

搜集汇总

背景与挑战

背景概述

NCSTP是一个用于非合作空间目标感知的多任务基准数据集，包含20万张模拟空间环境图像，支持目标检测、识别和组件分割任务，涵盖卫星、空间碎片和太空岩石等多种空间目标类别。数据集特别标注了卫星部件，并提供了不同规模的数据版本，适用于训练深度学习模型进行空间目标感知研究。