# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 2 Related Work
### Satellite Target Detection and Recognition Algorithms  
Due to the scarcity of publicly accessible datasets for key satellite component detection that faithfully replicate space capture scenes, the research on neural network-based methods for key satellite component detection remains in its nascent stages, and there are only a few studies focusing on this area.  
In key satellite component detection, [PERSON] et al. [30] utilized YOLOv5 (You Only Look Once version 5) for locating and identifying satellite components such as satellite bodies, solar panels, or antennas, followed by image classification. However, this algorithm is currently not sufficiently reliable and is not suitable for on-orbit deployment. Therefore, ongoing and future research focuses on integrating additional preprocessing and enhancement techniques, ensemble methods, and regression techniques for bounding boxes to improve the algorithm's detection accuracy and reliability.  
[PERSON] et al. [31] integrated extended convolutional high-resolution networks with online hard key point mining strategies to address imaging challenges in intricate space environments. Their enhanced network prioritizes occluded key points, extends the receptive field, and improves detection accuracy. Nevertheless, a disparity remains between virtual and real-space environments, and their study overlooked complex space motion scenarios.  
[PERSON] et al. [32] proposed a Region-based Convolutional Neural Network (R-CNN) that can accurately detect various key satellite components using optical images. However, the performance of the feature extraction network of R-CNN in detecting severely deformed components is still not satisfactory, making it unsuitable for on-orbit deployment.  
[PERSON] et al. [33] proposed a faster improved R-CNN for detecting small faulty satellite components under low illumination using image enhancement. However, they were unable to effectively address the issue of low detection rates for key satellite components under extreme illumination conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **SPEED** & **SPEED+** & **Dung** & **SPARK** & **Satellite-DS** \\ \hline Scene & Synthetic & Synthetic and real & Synthetic & Synthetic & Real \\ Illumination & Yes & Yes & No & No & Yes \\ Motion States & No & No & No & Yes \\ Occlusion & No & No & No & No \\ Resolution & 1920 \(\times\) 1200 & 1920 \(\times\) 1200 & 1280 \(\times\) 720 & 256 \(\times\) 256 & 1936 \(\times\) 1456 \\ Categories & 4 & 4 & 2 & 11 & 9 \\ Application & Pose estimation & Pose estimation & Pose estimation & Detection segmentation \\ Open Source & Yes & Yes & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison and analysis of existing datasets.  
In summary, although there has been some progress in the research on key satellite component detection methods, most detection methods have been trained using synthetic images. Even with the use of semi-physical simulation datasets, the challenges of extreme lighting conditions and occlusions leading to recognition difficulties, as well as low detection rates caused by motion states, have not been effectively addressed. Therefore, current key satellite component detection methods are unable to illustrate the impact of extreme conditions on detection accuracy in real capture scenes.