Review of Machine-Learning Approaches for Object and Component Detection in Space Electro-optical Satellites

The utilization of deep learning methods for the detection of space targets and components has received significant attention due to the continuous development of space missions. Effective detection and recognition of space target and components utilizing space-based electro-optical sensors is crucial for the intelligent perception and fine control of autonomous spacecraft. From an engineering application perspective, this article systematically reviews the current research status of space target detection and segmentation algorithms. This paper first summarizes the principle and characteristics of electro-optical sensors in space tasks and their application scenarios, and describes the common synthetic methods utilized for space target datasets. A summary of recent research on space target detection and component segmentation is given. Based on the research summary, we discuss several major issues for space target detection and segmentation. Research suggestions and future development directions are finally proposed.