Lightweight High-Resolution Keypoints Regression Network for Non-Cooperative Spacecraft Using Enhanced Attention Mechanisms

Extracting key features on non-cooperative space targets is a prerequisite for completing primary space missions such as target localization and attitude measurement. However, the space environment is characterized by extreme variations in light intensity, occlusions, and significant differences in image scales, making traditional feature extraction methods challenging to apply to extracting feature points from non-cooperative space vehicles. This paper proposes a deep learning-based keypoints regression algorithm to improve accuracy in non-cooperative space targets. Unlike traditional methods, which struggle with occlusions and lighting changes, our method leverages a hybrid attention mechanism and high-resolution networks to address these challenges effectively. Firstly, a keypoints regression network model for non-cooperative spacecraft is proposed, using a high-resolution network as the backbone. The model incorporates a hybrid attention mechanism during feature extraction and expands the receptive field during feature fusion to improve the accuracy of keypoints regression in occluded scenarios. Secondly, to address high memory usage issues and poor real-time performance caused by complex network models, a lightweight design is applied to the HRNet, ensuring accuracy and model efficiency. Finally, multiple experimental results validate the performance of the proposed deep learning-based keypoints regression algorithm for non-cooperative space targets.