Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

The detection and segmentation of key spacecraft components is a crucial prerequisite for the successful execution of on-orbit capture tasks; however, the existing spacecraft datasets are plagued by several problems. These include a lack of a unified dataset for component detection and segmentation, a lack of consideration of the motion states of the spacecraft, and a lack of consideration of extreme illuminations. These problems hinder the development of related research. In response to the above problems, this article aims to bridge this gap by releasing a dataset for detecting and segmenting key spacecraft components. In contrast to synthetic images, the proposed dataset is built via hardware-in-the-loop capture. The dataset contains two types of spacecraft, nine types of key components, three types of illuminations (normal illumination, low illumination, and high saturation), and two types of motion states (approaching phase and hovering phase); furthermore, we conduct extensive experiments on spacecraft detection and segmentation (Spacecraft-DS) with state-of-the-art methods for detection and segmentation tasks and provide comprehensive analysis. In addition, the dataset can also be used to evaluate many computer vision tasks, such as small object detection and so on. Our dataset and pretrained models will be released at https://github.com/spacecraftds/Spacecraft-DS .