An Edge AI Solution for Space Object Detection
This addresses the need for efficient collision assessment in near-Earth orbits, but it appears incremental as it builds on existing frameworks like YOLOv9.
The paper tackled the problem of real-time space object detection for collision avoidance by proposing a deep learning model combining SE layers, Vision Transformers, and YOLOv9, achieving high accuracy and low latency in detection tasks.
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.