## 4 Testing Experiment
### Masking Experiment  
Table 5 shows the results of the ablation experiments to analyze the impact of the different improvements on the model performance. In experimental group 1, the original YOLOv11 (including the SE module) is used with a mAP of 0.977, an FPS of 85.99, a parameter count of 2.59 M and a FLOPs of 3.21 G, which shows high detection performance and inference speed, but the higher parameter count and computational volume reflect the redundancy of the SE module in feature extraction, and there is still room for further optimization. In experimental group 2, after replacing the SE module with the more efficient ECA module, the mAP is increased to 0.986, the parameter volume is reduced to 2.35 M, and the FLOPs are reduced to 3.11 G, while the FPS is only slightly reduced to 85.79, which shows that the ECA module is better than the SE module in feature channel enhancement and can significantly improve the detection accuracy while reducing the complexity of the model, showing that it is better than the SE module for yoga pose detection. This shows that the ECA module is better than the SE module in terms of feature channel enhancement and can significantly improve the detection accuracy while reducing the model complexity.  
In experimental group 3, the removal of the Parallel Spatial Attention (PSA) module results in a mAP of 0.979, which is 0.002 higher than that of experimental group 1. Meanwhile, the FPS is increased to 89.88, the number of parameters is reduced to 2.34 M, and the FLOPs are the same as those of experimental group 2. The slight improvement in detection accuracy despite the removal of the PSA module suggests that the channel attention mechanism plays a central role in feature extraction after the introduction of the ECA module, while the spatial attention contribution of PSA is relatively limited. In addition, the significant improvement in FPS further confirms that the removal of the PSA module has significantly optimized the model inference efficiency. After the removal of all attention mechanisms in experimental group 4, the mAP recovers to 0.977, but the FPS decreases to 70.41, indicating that channel attention is a key factor in improving detection accuracy, while the removal of spatial attention plays a crucial role in optimizing inference efficiency.  
Figure 6: Visualization of YOLOv11-ECA detection performance on the validation set. (**a**) YOLOv11-ECA Validation sweet annotation example. (**b**) YOLOv11-ECA Validation set prediction results.  
The comprehensive analysis shows that the introduction of the ECA module is the key point to improve the performance of YOLOv11 in the yoga pose detection task. Compared to the SE module, the ECA module achieves higher feature enhancement through a lighter design, resulting in an increase in mAP to 0.986, while reducing the number of parameters and computation, demonstrating an excellent balance between efficiency and performance. In addition, the performance advantage of the ECA module can still be fully demonstrated after removing the PSA module, demonstrating the critical role of channel attention in feature extraction. The improved YOLOv11 achieves higher accuracy and inference efficiency in the yoga motion detection task, providing reliable technical support for subsequent research and application of related tasks.