基于边缘AI的太空物体检测模型，实现低延迟高精度卫星探测。
An Edge AI Solution for Space Object Detection
- 融合SE模块、ViT与YOLOv9构建边缘端检测模型。
- 在多种真实场景下实现多卫星高精度识别，延迟极低。
- 适合近地轨道卫星实时碰撞预警系统部署。
随着近地轨道空间资产增多，实现高效边缘人工智能（Edge AI）对太空物体检测（SOD）至关重要，可支持实时碰撞评估与规避。低地球轨道（LEO）卫星需以高精度和极低延迟探测其他物体。本文基于深度学习视觉感知提出一种边缘AI解决方案，设计了一种结合挤压-激励（SE）模块、视觉变换器（ViT）与YOLOv9框架的深度学习模型。在多种真实SOD场景中评估该模型性能，结果表明其能以高准确率和极低延迟检测多个卫星。
原文摘要 · Abstract (English)
Effective Edge AI for space object detection (SOD) tasks that can facilitate real-time collision assessment and avoidance is essential with the increasing space assets in near-Earth orbits. In SOD, low Earth orbit (LEO) satellites must detect other objects with high precision and minimal delay. We explore an Edge AI solution based on deep-learning-based vision sensing for SOD tasks and propose a deep learning model based on Squeeze-and-Excitation (SE) layers, Vision Transformers (ViT), and YOLOv9 framework. We evaluate the performance of these models across various realistic SOD scenarios, demonstrating their ability to detect multiple satellites with high accuracy and very low latency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。