Discrete Space-Target Trajectory Detection with a Linearity-Enhanced Network on Stacked Optical Images

Detecting discrete space-target trajectories from optical image sequences is important for ground-based space situational awareness, but direct sequence processing can be computationally expensive and sensitive to weak target responses. This paper presents LeTD, a linearity-enhanced trajectory detection framework built on compact sequence-level projections and a YOLO11n oriented bounding box detector. The aligned image sequence is reduced to maximum, median, and temporal projection maps, and sine–cosine temporal encoding is used to inject frame-index information into the standard three-channel detector input. Additional angle and endpoint supervision terms are introduced to improve localization of sparse linear trajectories while keeping the YOLO11n-OBB backbone and detection head unchanged. On an expanded real test set containing 82 sequences and 155 trajectories, LeTD achieves a track detection rate of 0.7742, a complete track rate of 0.6903, and a false-component rate of 0.2055 per frame under a unified GT-centric trajectory protocol. The method requires 0.52 s per sequence on average on the evaluated workstation platform, including representation construction and detector inference.