# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 1 Introduction
These issues make it difficult to identify key components in images and greatly limit their detection in malfunctioning target satellites, leading to missed or false detections [18]. Therefore, a dataset that accurately reproduces the spatial lighting conditions, motion states, and occlusions encountered by key satellite components must be urgently established. It would undoubtedly hold immense value in advancing neural network-based object detection applications in space [19; 20; 21]. However, existing satellite datasets [22; 23] predominantly comprise synthetic images, posing challenges in accurately representing real illumination, motion states, and occlusion conditions of space targets. In order to advance the development of key satellite component detection methods, the TYCOS dataset, collected using a semi-physical system within a darkroom and specifically tailored for key satellite component detection in on-orbit capture missions, is introduced in this paper. The main contributions of this paper can be summarized as the following three aspects. * A unified dataset for the detection of key satellite components is established, meticulously crafted to accurately represent key satellite components. In addition, employing a semi-physical collection system, this dataset accurately replicates the scenarios encountered in space capture missions. In terms of illumination, it encompasses the following three distinct lighting conditions: normal illumination, low illumination, and high saturation. Regarding motion states, the dataset simulates the hovering and approaching states of the observing satellite in the capture missions, as well as the rolling state of the target satellite, effectively linking imaging effects with the motion states of targets. Furthermore, in addressing occlusion challenges, the dataset includes scenarios where the body of the target satellite occludes its own solar panels. Compared with other existing datasets, our dataset can better reflect the real-space imaging effect and reflect the real-space conditions. * A comprehensive validation analysis of current mainstream object detection algorithms on the dataset is conducted, in order to establish initial detection benchmarks. Eight sets of classic and advanced detection models are evaluated in this paper. Aiming to address the challenges posed by the higher demand for the detection accuracy of space targets, appropriate backbones are selected to improve the initial detection benchmarks. * Images capturing all spatial directions and distances of up to 5 m are included in the proposed dataset. This dataset faithfully replicates direct sunlight in satellite observation images, offering an unparalleled quantity and quality of images that depict key components of satellite models, facilitating a comprehensive evaluation of the robustness of key satellite component detection across a wide array of high-fidelity environments. The remaining sections of this paper are organized as follows. Section 2 introduces existing datasets related to space scenes. Section 3 introduces details of the generation process of the dataset proposed in this paper. Section 4 presents the experimental process and results. Section 5 provides the conclusions.