End-to-End On-Orbit Objects Detection with ConvNets

As space activities expand, the quantity of space debris also increases, posing significant risks to spacecraft and infrastructure. Space situational awareness (SSA) is essential for avoiding collisions and limiting the generation of extra debris. Accurate and efficient detection of space objects plays a critical role in achieving this goal. Our research focuses on the development of detection algorithms that are both precise and quick, taking into account the real-time and safety of spacecraft operations in orbit. For the first time, we take a fully Convolutional Neural Networks (ConvNets) to run the query-based end-to-end object detection for SSA. We further compare its performance with the newest YOLOv9 algorithm. This is an innovative attempt at SSA. First of all, it does not require predefined a priori anchor boxes or complex post-processing strategies such as Non-Maximum Suppression (NMS), and can directly achieve end-to-end target detection. Secondly, the fully ConvNets are selected as the basic framework, which not only retains the advantages of self-attention mechanism, but also greatly improves the computing efficiency. These methods show outstanding performance on the challenging SPARK data set. The fully ConvNets approach achieves end-to-end detection by utilizing the query attention mechanism, excluding the need for complicated post-processing in traditional object detection methods and with higher efficiency. YOLOv9 involves an enhanced feature pyramid fusion and a more powerful detection head, potentially resulting in higher precision. Following that, we will thoroughly assess the speed, accuracy, and trade-offs of the two algorithms using actual data sets in order to deliver an efficient and dependable solution for detecting targets in aerospace sensing missions.