Although space targets have different shapes, sizes and intensities, their distributions share certain commonalities. However, it is difficult to summarize a generalized distribution function for space targets. Moreover, most of the existing methods based on deep learning are not suitable to use directly because of the size of targets and the cost of manual labeling for a full image. In this paper, we proposed a pattern for space target detection based on a convolutional neural network (CNN) to learn essential features of the targets from data. In the processing stage, the background is estimated and removed. Then, image techniques are used to search and process region proposals. Different sizes of region proposals are recognized by a discriminator, which is built upon a small CNN trained with the data of several specific targets. Finally, a non-maximum suppression (NMS) operation is used to remove redundant targets. In the network structure, to further enhance the influence of the effective area, the parameters calculated from the center region of the input are utilized as guidance information and added to the features before the full connection. Moreover, the bias loss is applied to increase the weights of unique features. The experimental results demonstrate the outstanding performance of the proposed method in terms of the number of detected targets, accuracy rate and false alarm rate compared with baseline methods. In particular, the proposed method has a simple network structure and a lower computational cost which can be further promoted and implemented in actual engineering.

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.