In order to solve the problems of poor scalability, low generality and high computational cost of the traditional method using single object CNN model, and optimize the performance of multi-objective method. In this paper, a single-stage network architecture for multi-objective 6D attitude estimation is proposed, and a multi-branch feature extraction decoder is designed to capture and aggregate detailed features effectively. This paper proposes a feature optimization and screening module, which filters input features to extract multi-scale features. Combining the above two, a new feature pyramid structure is designed to improve the overall performance of the network and improve the pose estimation effect of occlusion. The experiments are carried out on synthetic data set LINEMOD and Occluded LINEMOD. The results show that the proposed method has achieved significant improvement in the processing of blocked object scenes. Compared with the most advanced methods such as PyraPose, SD-Pose and CASAPose, the proposed method has increased the ADD/S-Recall index by 43.1%, 16.1% and 12%, respectively. It performed better when the number of targets is small, increasing performance by 17% when the number of targets is 4. The ablation experiment further verifies the effectiveness of each module. By introducing multi-branch feature extraction decoder, feature optimization and screening module, and feature pyramid structure, the proposed single-stage multi-objective network architecture can process any number of targets by training only one network, and can perform 6D pose estimation better under the condition of synthetic data. Experimental results verify the effectiveness of the proposed method.