High-Precision Domain Adaptive Detection Method for Noncooperative Spacecraft Based on Optical Sensor Data
Gaopeng Zhang, Zhe Zhang, Jiahang Lai, Guangdong Zhang, Hao Ye, Hongtao Yang, Jianzhong Cao, Hubing Du, Zixin Zhao, Weining Chen, Rong Lü, Changqing Wang
Abstract
The accurate detection of noncooperative spacecraft based on optical sensor data is essential for critical space tasks, such as on-orbit servicing, rendezvous and docking, and debris removal. Traditional object detection methods struggle in the challenging space environment, which includes extreme variations in lighting, occlusions, and differences in image scale. To address this problem, this article proposes a high-precision, deep-learning-based, domain-adaptive detection method specifically tailored for noncooperative spacecraft. The proposed algorithm focuses on two key elements: dataset creation and network structure design. First, we develop a spacecraft image generation algorithm using cycle generative adversarial network (CycleGAN), facilitating seamless conversion between synthetic and real spacecraft images to bridge domain differences. Second, we combine a domain-adversarial neural network with YOLOv5 to create a robust detection model based on multiscale domain adaptation. This approach enhances the YOLOv5 network’s ability to learn domain-invariant features from both synthetic and real spacecraft images. The effectiveness of our high-precision domain-adaptive detection method is verified through extensive experimentation. This method enables several novel and significant space applications, such as space rendezvous and docking and on-orbit servicing.