用视觉变压器提升小卫星的太空物体检测能力，兼顾精度与资源消耗。
Sensing for Space Safety and Sustainability: A Deep Learning Approach with Vision Transformers
- 将视觉变压器融入高效网络架构，分离卷积与注意力路径提升效率。
- 在太空检测数据集上达到95%的mAP50，计算量降低超5.0 GFLOPs。
- 适合资源受限的小卫星平台，适用于实时太空安全监测场景。
低地球轨道中小卫星数量激增，为全球数字服务带来可能，但动态空间环境、大量空间物体、复杂大气条件及突发事件易引发安全风险，威胁太空运行与可持续性。亟需高效响应的星载物体检测（SOD）方案，以应对碰撞风险，同时适配小卫星平台的资源约束。本文提出基于深度学习的SOD方法，设计两种新模型GELAN-ViT与GELAN-RepViT，将视觉变压器（ViT）融合进广义高效层聚合网络（GELAN）架构，通过分离卷积与ViT路径解决原有局限。在SOD数据集上，模型实现约95%的mAP50，GFLOPs降低超过5.0；在VOC 2012数据集上，mAP50达≥60.7%，GFLOPs降低超5.2，优于当前最优模型YOLOv9-t。
原文摘要 · Abstract (English)
The rapid increase of space assets represented by small satellites in low Earth orbit can enable ubiquitous digital services for everyone. However, due to the dynamic space environment, numerous space objects, complex atmospheric conditions, and unexpected events can easily introduce adverse conditions affecting space safety, operations, and sustainability of the outer space environment. This challenge calls for responsive, effective satellite object detection (SOD) solutions that allow a small satellite to assess and respond to collision risks, with the consideration of constrained resources on a small satellite platform. This paper discusses the SOD tasks and onboard deep learning (DL) approach to the tasks. Two new DL models are proposed, called GELAN-ViT and GELAN-RepViT, which incorporate vision transformer (ViT) into the Generalized Efficient Layer Aggregation Network (GELAN) architecture and address limitations by separating the convolutional neural network and ViT paths. These models outperform the state-of-the-art YOLOv9-t in terms of mean average precision (mAP) and computational costs. On the SOD dataset, our proposed models can achieve around 95% mAP50 with giga-floating point operations (GFLOPs) reduced by over 5.0. On the VOC 2012 dataset, they can achieve $\geq$ 60.7% mAP50 with GFLOPs reduced by over 5.2.
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