Based on the improved YOLOv8n pose estimation algorithm

This paper presents a pose estimation algorithm based on an improved YOLOv8n, aimed at addressing the performance limitations of existing methods under resource-constrained conditions. Firstly, the NAM attention mechanism module is introduced, which combines channel and spatial attention to effectively capture multimodal information across different dimensions without increasing computational load. The C2f-GC module is designed to generate multi-scale features through grouped feature processing and pointwise convolution, optimizing parameter count and improving computational speed. The SPD layer is integrated to enhance the model's ability to handle small-sized features by compressing spatial information into the depth dimension. Finally, the improved backbone network is substituted for the original network in the YOLO-6D algorithm, with corresponding function adjustments made to achieve more accurate 6D pose estimation. Ablation experiments conducted on the Pascal VOC2012 dataset demonstrate that the parameter count of the improved model is reduced by 3% compared to the original model, while mAP0.5 and mAP0.5-0.95 are improved by 1.5% and 0.7%, respectively, validating the effectiveness of the optimizations. Comparative pose estimation experiments on the LINEMOD dataset show that the proposed algorithm achieves an average accuracy of 93.54% across 13 test objects, representing a 3.17 percentage point improvement over the original algorithm, thereby significantly enhancing performance.