Efficient End-to-End Spacecraft Component Detection Based on Residual Self-attention and Separated Set Matching
The rapid development of space technology in China has led to a multitude of spacecraft launches.However,these spacecraft are expected to experience the influence of uncontrollable factors such as radiation and temperature changes during operation.These changes may impede the accurate measurement of spacecraft positions and behaviors by ground stations,thereby impacting on-orbit services such as communications and docking,as well as grappling between spacecraft.To solve these problems,the present study first annotates the SDDSP spacecraft dataset which encompasses detection,segmentation,and component recognition with 3 117 spacecraft images and 11 001 detection targets.An efficient end-to-end spacecraft component detection model is then proposed based on Residual Self-attention(RS)and Separated Set Matching(SSM)in space on-orbit services.The RS mechanism is introduced on the basis of the Sparse DEtection TRansformer(DETR)model to solve the problem of sparse tokens,which slows convergence and degrades the prediction accuracy of the model.Furthermore,SSM is deployed to address the phenomenon of instability that may occur in the process of dichotomous matching.The experimental results show that the Average Precision(AP)and convergence speed of the model are improved by 17.9 percentage points and 10 times,respectively,compared with those of the baseline DETR model,as well as 3.1 percentage points and 20％,respectively,compared with those of the Sparse DETR model.
spacecraft component detectionSparse DERT modelResidual Self-attention(RS)Separated Set Matching(SSM)aircraft dataset
万方数据
维普
