A keypoint detection algorithm for spacecraft in complex background

Accurate pose estimation of spacecraft is crucial for in orbit services and space debris cleaning missions. The current mainstream methods are mostly based on keypoint detection. But, accurately detecting the keypoints of spacecraft is a major challenge in complex space backgrounds. To address this issue, we introduced a segmentation mask attention interaction method to reduce the network's excessive focus on background information and enhance attention to the spacecraft subject. Besides, in order to reduce the deviation of keypoint positions, we add a keypoint refinement module, which regresses the offset value between the initial coordinates of keypoint and their true positions, thereby fine-tuning the keypoint coordinates and improving the convergence ability and accuracy of the network. Through these improvements, our method has shown significant performance in accurately detecting keypoints of spacecraft, providing reliable technical support for space exploration missions.