A Large-Scale Synthetic Benchmark Dataset for Non-Cooperative Space Target Perception.

The automatic, accurate perception of targets in space is a crucial prerequisite for many on-orbit aerospace missions. Therefore, research on perception technologies within spaceborne images is meaningful. The development of deep learning has revealed its potential for application to space target perception. However, implementing deep learning models requires large-scale labelled datasets. Therefore, we build a multitask synthetic benchmark space target dataset, NCSTP, to address the limitations of current datasets. First, we collect and modify various space target models for satellites, space debris, and space rocks. By importing them into a realistic space environment simulated by Blender, 200,000 images are generated with different target sizes, poses, lighting conditions, and backgrounds. Then, the data are annotated to ensure the dataset supports simultaneous space target detection, recognition and component segmentation. All data can be used for training space target detection and recognition models. We further annotate the components of each satellite for component segmentation. Finally, we test a series of state-of-the-art object detection and semantic segmentation models on the dataset to establish a benchmark.