BiSlim-6D:A 6D pose estimation network for efficient feature decoupling and fusion

6D pose estimation is a crucial component of robotic interaction tasks and remains an active research direction in the field of computer vision. Therefore, developing a 6D pose estimation algorithm that simultane-ously achieves high speed and high accuracy is essential. Recently, there has been a trend of utilizing real-time and accurate YOLO series methods for 6D pose estimation tasks. In this work, we propose a new 6D pose estimation network, BiSilm-6D, which uses CSPDarknet53 as the backbone and incorporates BiFPN built following the Slim-neck paradigm as the feature fusion network. We evaluate the contributions of CSPDarknet53 and BiSlim-neck to the performance of 6D pose estimation and attempt to explain the reasons for these contributions. Furthermore, through comparative experiments, we demonstrate that BiSlim-6D exhibits strong overall performance among current 6D pose estimation networks. Our proposed method achieves an accura-cy of 98.78 on the 2D reprojection metric and 81.51 on the ADD -S metric. The proposed method has the potential for practical application in relevant tasks in the future.