The rapid proliferation of non-cooperative spacecraft and space debris in orbit has precipitated a surging demand for on-orbit servicing and space debris removal at a scale that only autonomous missions can address, but the prerequisite autonomous navigation and flightpath planning to safely capture an unknown, non-cooperative, tumbling space object is an open problem. This requires algorithms for real-time, automated spacecraft feature recognition to pinpoint the locations of collision hazards (e.g. solar panels or antennas) and safe docking features (e.g. satellite bodies or thrusters) so safe, effective flightpaths can be planned. Prior work in this area reveals the performance of computer vision models are highly dependent on the training dataset and its coverage of scenarios visually similar to the real scenarios that occur in deployment. Hence, the algorithm may have degraded performance under certain lighting conditions even when the rendezvous maneuver conditions of the chaser to the target spacecraft are the same. This work delves into how humans perform these tasks through a survey of how aerospace engineering students experienced with spacecraft shapes and components recognize features of the three spacecraft: Landsat, Envisat, Anik, and the orbiter Mir. The survey reveals that the most common patterns in the human detection process were to consider the shape and texture of the features: antennas, solar panels, thrusters, and satellite bodies. This work introduces a novel algorithm SpaceYOLO, which fuses a state-of-the-art object detector YOLOv5 with a separate neural network based on these human-inspired decision processes exploiting shape and texture. Performance in autonomous spacecraft detection of SpaceYOLO is compared to ordinary YOLOv5 in hardware-in-the-loop experiments under different lighting and chaser maneuver conditions at the ORION Laboratory at Florida Tech.

翻译：在轨非合作航天器与空间碎片数量的快速增长，催生了仅靠自主任务才能解决的大规模在轨服务与空间碎片清除需求。然而，实现对未知非合作翻滚目标的安全捕获所需的前提——自主导航与航迹规划——仍是一个开放性问题。这需要实时自主航天器特征识别算法，以精确定位碰撞危险源（如太阳能板、天线）及安全对接特征（如卫星本体、推进器），从而规划安全有效的飞行轨迹。现有研究表明，计算机视觉模型的性能高度依赖训练数据集及其对部署中真实场景的视觉相似性覆盖程度。因此，即便追踪器与目标航天器的交会机动条件相同，算法在不同光照条件下仍可能出现性能退化。本文通过调研航天工程专业学生对三颗航天器（Landsat、Envisat、Anik）及和平号轨道器的特征识别经验，深入探究人类执行该任务的过程。调查显示，人类检测过程中最常见的模式是基于形状与纹理特征（天线、太阳能板、推进器、卫星本体）进行判断。本文提出新型算法SpaceYOLO，将前沿目标检测器YOLOv5与基于上述人类启发决策过程（利用形状与纹理特征）的独立神经网络相融合。在佛罗里达理工学院ORION实验室开展的硬件在环实验中，对比评估了不同光照与追踪器机动条件下SpaceYOLO与常规YOLOv5在自主航天器检测中的性能表现。