STAR: Spatial and Temporal Context-Aware Network for Resident Space Object Detection

Most resident space object (RSO) detection methods are designed based on the observation mode of the telescope, focusing the core of RSO detection on point-like or streak-like object detection. These RSOs are typically small in size and weak in energy, while noise and stars will affect the detection. Therefore, these methods adopt complex processing pipelines or design neural networks to achieve detection. However, for dim RSOs, the features about point or streak are not prominent, making it difficult for these methods to maintain stable detection performance. In this paper, we propose a network called STAR Spatial and Temporal Context-Aware Network for Resident Space Object Detection, which attempts to use spatial and temporal context information as supplementary cues to enhance detection performance. STAR introduces Spatial Context Extraction module that fuses small and large kernel convolutions to capture fine morphological feature and surrounding context information, respectively, and Temporal Context Extraction module that employs deformable attention to adaptively model motion patterns across frames. Experiments on a self-collected dataset composed entirely of real images show that STAR exhibits excellent detection capability. On the public dataset SpotGEO, STAR achieves 94.93% F1 score and 29646.49 MSE, surpassing the champion of the SpotGEO challenge, and outperforming many current deep learning-based methods.