The growing number of space objects raises collision risks and demand for
on-orbit maintenance. Since most servicing tasks require observing the target,
relative pose estimation is key for debris removal and satellite servicing. The
complex orbital environment poses significant challenges for conventional
feature extraction networks.. To address these challenges, this paper presents a
multi-task convolutional neural network for space target pose estimation. The
network uses a MobileNetV3-based encoder to extract image features and an
Adaptively Spatial Feature Fusion (ASFF) module for adaptive multi-scale feature
fusion. Although MobileNetV3 and ASFF have rarely been combined in the aerospace
field, their combination offers significant advantages in low parameter count
and reduced computational load, showing strong potential for spacecraft pose
estimation. Two parallel prediction heads are employed: an EfficientPose head
consisting of classification, bounding box regression, rotation matrix
regression, and translation vector regression, together with a Heatmap head.
This design leads to more robust and accurate pose estimation. The loss function
follows a multi-prediction-head joint approach, where each component is weighted
to form the final loss for multi-task optimization. The network is trained,
validated, and tested on a simulation dataset of the DAWN satellite built with
Unity. The dataset includes illumination variations, large distance changes, and
multi-view target appearances, thus capturing the complex scenes typical of
vision-based recognition tasks. Experimental results show that after 40 training
epochs, the model achieves an average position error of about 0.05 m and a
rotation error of about 0.8° on the test set. These results demonstrate the
effectiveness and accuracy of the proposed network for space target pose
estimation.