# SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
## 3 Results and Analysis
### Optimization Experiments for SMR
The results are shown in Table 3, which indicates that increasing the value of _SCALE_ in the training process can improve the initial mask quality, but the optimal mask quality still needs to be achieved by adjusting the _SCALE_ in the prediction process, and there is no significant relationship between the optimal mask quality and the _SCALE_ value in the training process. In order to visually explain the mechanism of the effect of variation in _SCALE_ on the prediction mask, the anchors, region proposals, and masks of the SMR in the prediction process under different _SCALE_ values were tracked, and one of the representative results \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{_SCALE_} & & & & & \\ \hline
**Train** & **Predict** & **IoU** & **F1** & **Parameters/M** & **Memory/Mb** & **t/ms** \\ \hline
16 & 16 & 0.648 & 0.790 & 29.8 & 808.0 & 123.8 \\
16 & 24 & 0.803 & 0.896 & - & - & - \\
16 & 30 & 0.828 & 0.999 & - & - & - \\
24 & 24 & 0.782 & 0.890 & 29.8 & 808.0 & 123.8 \\
24 & 30 & 0.819 & 0.904 & - & - & - \\
24 & 48 & 0.830 & 0.910 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of SMR under training at different _SCALE_. Figure 7: Effect of using different _SCALE_ and _RATIO_ on the segmentation performance of SMR and Mask R-CNN. (**a**): Mask R-CNN. (**b**): SMR. The Evaluation index is IoU. 0.2\(\times\) represents 0.2 times the original _RATIO_, 1.0\(\times\), and 5.0\(\times\) ditto. was selected as shown in Figure 8. For \(SCALE=8\), 16, 24, 32, and 40, the model found the exact location of the stubble rows and presented at least one regional proposal centered on each of the rows, the difference being that for \(SCALE=8\), the anchor size was small and the resulting region proposals were similarly small, covering only a small portion of the rolled stubble rows and therefore only a small portion of the mask. As the \(SCALE\) increases, the size of the anchors increases, and so does the size of the region proposals. For the region proposals with the center point on the stubble row, the larger the size means that the complete stubble row is covered, and therefore the mask generated in the region proposals is more complete; at \(SCALE=32\), the mask was able to cover almost the complete rolled rice row. As \(SCALE\) continued to increase to 40 and 48, the anchors continued to increase, while the region proposals did not change significantly; in turn, the mask changes were not obvious. Figure 8: Visualization of the effect of \(SCALE\) changes on SMR, the white dots in the figure indicate the center points of the boxes, and for each column in the figure: (**c**) shows the final masks output by the model; (**b**) shows the region proposals and the masks generated within each region proposal (After NMS); (**a**) shows the anchors corresponding to the region proposals in row (**b**), and the region proposals in row (**b**) are derived from the anchors of the same color in row (**a**). The reason why the size of the region proposals increased as the anchor increased is that, according to Table A1, as _SCALE_ increased, the size of the anchors increased, and from Equation (1), the larger the size of the anchors, the larger the size of the region proposals, and as _SCALE_ increases from 32 to 40 and 48 in Figure 8, the size of the region proposals does not change significantly because the region proposals that are outside the scope of the image will be cropped out.