SCPose-MLite: A Lightweight Neural Network for On-Orbit Attitude Estimation of Space Targets

Accurate pose estimation of space targets is of great significance for conducting on-orbit rendezvous, space debris removal and other tasks.In this paper,we propose a lightweight deep learning model, SCPose-MLite, which is based on the deep learning framework of URSONet and adopts MobileNet-V2 as the backbone network.By integrating the mixed pooling module, SCPose-MLite further enhances the generalization ability of the model while maintaining high accuracy.The experimental results show that SCPose-MLite is close to URSONet in terms of pose estimation accuracy, and at the same time, it has improved the generalization factor by 2.32 times, which can better meet the actual needs of space target pose estimation.Furthermore, this paper attempts to run the lightweight model on different devices, demonstrating that the training optimization brought by the mixed-precision quantization strategy and high-computing-power devices may fail in the training of low-parameter networks.