Aiming at the problem of six-degree-of-freedom pose estimation for noncooperative targets in space, this research involved designing a lightweight network named LSPENet based on convolutional neural networks, which could be used to realize end-to-end pose estimation without manually designing features. We used depth-separable convolution and efficient channel attention (ECA) to form the basic module, which balanced the complexity and accuracy of the network. One branch was designed for location estimation using direct regression, and another branch was designed for orientation estimation by introducing soft-assignment coding. Experimental results on the URSO dataset show that soft-assignment coding-based orientation estimation exhibits substantially lesser errors than direct regression-based orientation. Further, compared with the other end-to-end pose estimation network, the proposed network reduces parameter count by 76.7% and decreases single-image inference time by 13.3%, while simultaneously improving location estimation accuracy by 54.6% and orientation estimation accuracy by 57.8%. Overall, LSPENet provides a new idea for monocular visual pose estimation on board.