# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
## 4 Experiments
### Validation and Analysis
#### 4.3.4 Result Analysis  
Table 2 displays the experimental performance comparison of human pose estimation between our approach and some other current advanced methods on the COCO 2017 validation dataset. Our model experiment data are specifically displayed in the bold font. The results showed that when the input data size was fixed at 640 \(\times\) 640 pixels, compared with the original baseline Yolo-Pose model, our proposed network reduced the parameter amount by 0.8 M, decreased the calculation cost by 6.0 GFLOPs, achieved 5.7% improvement on mAP, and showed a 5.7% significant improvement on AP50. In the case of images with the same input size, compared to large, high-resolution bottom-up human pose estimation networks such as HigherHRNet and DEKR, which have approximately 40 times the computation cost and 10 times the number of parameters, the method we put forward also had 1.8% and 0.6% improvements on AP50. The results of the experiment on the COCO 2017 validation dataset demonstrated that our approach can greatly enhance the previous Yolo-Pose network's precision and convergence speed, providing an excellent alternative to complex and large-scale human pose estimation methods and enhancing further research and exploration of human keypoint detection methods without heatmaps [46]. Figure 6 shows the AP50 comparison of human posture estimation methods with various network computations on the COCO 2017 validation dataset. The figure shows that, when compared to bottom-up key point detection methods using large networks with higher accuracy and high complexity, such as HigherHRNet and DEKR, the proposed network retained a pretty high level of human key point detection accuracy while simula \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **Params** & **GMACS** & **AP [0.50.951\%]** & **AP50\%** & **AR** \\ \hline OpenPose [19] & - & - & - & - & 61.8 & 84.9 & 66.5 \\ Hourglass [47] & Hourglass & 512 \(\times\) 512 & 277 s M & 413.8 & 56.6 & 81.8 & - \\ HRNet [18] & HRNet-W32 & 512 \(\times\) 512 & 28.5 M & 77.8 & 64.1 & 86.3 & - \\ HigherHRNet [22] & HRNet-W48 & 640 \(\times\) 640 & 63.8 M & 308.6 & 68.4 & 88.2 & - \\ DEKR [48] & HRNet-W32 & 512 \(\times\) 512 & 29.6 M & 90.8 & 67.3 & 87.9 & 72.4 \\ DEKR [48] & HRNet-W48 & 640 \(\times\) 640 & 65.7 M & 283.0 & 70.0 & 89.4 & 75.4 \\ YOLOv56-pose(baseline) [14] & Darknet\_csp-d53-s & 640 \(\times\) 640 & 72.8 M & 10.2 & 57.5 & 84.3 & 65.2 \\ YOLOv56-pose [14] & Darknet\_csp-d53-s & 960 \(\times\) 960 & 15.1 M & 22.9 & 62.9 & 87.6 & 69.8 \\ Ours & Darknet\_csp-d53-s & **640 \(\times\) 640** & **64.4 M** & **7.2** & **63.2** & **90.0** & **71.9** \\
**Ours** & **Darknet\_csp-d53-m** & **640 \(\times\) 640** & **19.6 M** & **23.5** & **67.4** & **91.0** & **74.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Accuracy of Different Methods on COCO 2017. To further investigate the performance and operational efficiency of our method, we compared the inference speed and corresponding average accuracy of the proposed model with other classical human keypoint detection methods on the COCO 2017 validation dataset, especially for some lightweight models. FPS (frames per second) served to quantify the inference speed. The experimental comparison results are shown in Table 4 and Figure 8. The bold text in Table 4 shows the results of our model experiment. According to the data, our approach not only had higher detection accuracy than the original network, but it also sped up inference. HigherHRNet employs HRnet as its backbone. Its network structure features numerous branches and parallelism, which have a detrimental impact on the network's actual inference performance. Not only that, but our approach retained a comparable inference speed and significantly outperformed lightweight human posture estimation networks such as EfficientHRNet in terms of detection accuracy.