# Buckle Pose Estimation Using a Generative Adversarial Network
## 4 Experiments and Results
### Ablative Study  
To evaluate the impact of each module on overall performance, ablation experiments were conducted using the same training parameters and datasets for the cavity convolution and refinement edge modules. The refinement edge module was used to assist in segmentation network training and the inference phase was not used. As shown in Table 4, the accuracy of the center point estimation increased from 93.2 to 98.7% after adding the dilated convolutions to expand the network receptive field, and the accuracy of angle estimation increased from 93.0 to 98.6%. When using the refinement edge module of GAN to assist generator training, the network better refined the contours of the generated images. The accuracy of central point estimation increased by 0.8% and that of angle estimation increased by 0.7%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **ODS** & **OIS** & **AP** & **R50** \\ \hline Hed & 0.9720 & 0.9835 & 0.9680 & 0.9775 \\ RCF & 0.9725 & 0.9860 & 0.9750 & 0.9885 \\ DexiNed & 0.9660 & 0.9735 & 0.9400 & 0.9905 \\ PidiNet & 0.9250 & 0.9540 & 0.9580 & 0.9960 \\ ours & 0.9740 & 0.9850 & 0.9775 & 0.9970 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation indexes of different methods.  
Figure 10: Results of different methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Center Point** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Maximum Error** \\ **of Center Point** \\ **(Pixel)** \\ \end{tabular} & \
\begin{tabular}{c} **Angle Accuracy** \\ **(Degree)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Angle Error** \\ **(Degree)** \\ \end{tabular} \\ \hline Hed & 94.7\% & 18.27 & 97.2\% & 2.86 \\ RCF & 96.5\% & 12.68 & 98.4\% & 2.93 \\ DexiNed & 93.6\% & 13.45 & 92.7\% & 4.99 \\ PidiNet & 95.2\% & 14.31 & 96.4\% & 3.56 \\ ours & 99.5\% & 7.36 & 99.3\% & 1.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy and error values of different methods.  
The improvements highlighted samples with poor regression effects regarding the edge extraction network. The regression of contours and lines was more refined to be closer to the actual values, and the effect image and difficult sample result image were refined. As shown in Figure 11, when only the edge extraction network is used to regress the target, a double contour phenomenon may occur, which affects the regression results. When dilated, the convolution is added to increase the receptive field, whereas the double contour and shape line regression phenomena are reduced to a certain extent. However, the regression contour was rougher. Again, leveraging the GAN method, the discriminator and generator confrontation training was added to distinguish the authenticity of the generated image; thus, the double contour phenomenon disappeared.  
After obtaining the outer contour and the center edge line of the target using the proposed method, the minimum circumscribed rectangle of the outer contour is estimated, and the center point of the circumscribed rectangle is taken as the center point of the target location. The center edge line is fitted using the least square method, and the slope obtained is used as the rotation angle of the target relative to the horizontal position. Some of the final results are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Center Point Accuracy** & **Angle Accuracy** \\ \hline Edge Network & 93.2\% & 93.0\% \\ Dilate & 98.7\% & 98.6\% \\ Refinement Edge & 99.5\% & 99.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ablation experiments of different modules.  
Figure 11: Comparison of the results of ablation experiments.  
Figure 12: Final result image (the center point and the rotation angle are obtained by the least circumscribed rectangle and the least square method for the outer contour and the center edge line, respectively).