# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 2 Related Work  
### Existing Space Scene Datasets  
Currently, with the sharp increase in demand for space situational awareness, many scholars have released satellite datasets aimed at advancing tasks such as target detection and attitude estimation using deep learning techniques [24; 25]. [PERSON] et al. [26] released the Satellite Pose Estimation Dataset (SPEED). SPEED has been the most widely used satellite dataset in recent years. However, SPEED is primarily used to address satellite pose estimation problems rather than executing satellite detection tasks. Furthermore, SPEED relies on synthetic images for training and validation, which are easy to produce in bulk but cannot simulate the inherent visual characteristics and lighting variability of the images of target satellites. Therefore, there are limitations in applying SPEED in space capture missions. In a similar vein, [PERSON] et al. [22] introduced SPEED+ to tackle satellite pose estimation and navigation challenges. This dataset comprises both synthetic and hardware-simulated images. However, it cannot be used for executing space target detection tasks. The sole consolidated dataset that is presently accessible for satellite detection endeavors was made publicly available by [PERSON] et al. [27]. However, this dataset specifically focuses on detecting satellites as a whole, excluding their key components, and comprises synthetic images gathered primarily from the internet. Since synthetic images differ significantly from real images in terms of illumination, this dataset cannot reflect the kinestate of satellites in space capture tasks. [PERSON] et al. [28] introduced SPARK, a multimodal image dataset tailored for space target detection under realistic space simulation scenarios. As for modalities, it comprises RGB and depth images including 11 types of objects, 10 of which are related to satellites, and additionally 11 types of space debris. Nevertheless, in both detection and segmentation tasks, SPARK only labels satellite bodies. It overlooks the role of key components in real capture missions. Additionally, the synthetic images in SPARK still exhibit significant differences from real images in terms of the illumination and motion states of space targets. [PERSON] et al. [23] introduced the Satellite-DS, a simulated environment-based space target detection image dataset. This dataset was acquired within a darkroom environment constructed to mimic space conditions using hardware. It encompasses two types of satellite, nine types of key components, and three types of illuminations (normal, low, and high saturation). Additionally, it incorporates two types of motion states (approach and hover phases). However, it falls short in addressing occlusion challenges arising from the target satellite bodies obscuring their own key components. Furthermore, there is a lack of comprehensive analysis regarding the validation of current mainstream target detection algorithms on the dataset. The dataset also fails to adequately tackle the recognition difficulties stemming from extreme illuminations and the low detection rate induced by motion states. Table 1 presents an analysis of the existing satellite datasets, listing the collection scenarios, illuminations, satellite motion states, occlusion of key components, image resolution, key component categories, task types, and open-source status. The results of the analysis can be summarized as follows. Firstly, existing satellite datasets mostly consist of synthetic images, making it difficult to reflect practical illuminations, motion states, and occlusion. However, extreme lighting, satellite motion states, and occlusions in observation and capture scenes significantly impact detection precision. Secondly, the majority of the current datasets are geared towards pose estimation in space-related scenarios, with few datasets considering key ingredient analysis, which is a deficiency that may lead to low detection rates.