# SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
## 3 Results and Analysis
### SMR Backbone Network Simplification Experiment  
The performance of SMR-RS is shown in Table 4, compared to SMR-40, which was obtained after optimizing SMR, the total parameters are reduced by 7.4%, the prediction speed is increased by 37.5%, the GPU occupation is reduced by 32.1%, the IoU value is increased by 1.5%, and the F1 value is increased by 0.6%. To achieve these, statistics are presented in the following paragraphs on the utilization of feature maps by SMR as a basis for targeted censoring of the backbone network.  
In the process of predicting the test set using SMR, it was found that most of the anchors with high scores had relatively similar sizes. Since the size of anchors generated by the same feature map is the same, it is guessed that most of the high-scoring anchors may come from the same feature map, and the feature map attribution of the anchors can be inferred from Table A1 based on the size of the anchors. For example, it is known that the H \(\times\) W of the area of one of the highest scoring anchors is 4096, and the hyperparameter _SCALE_ is 8, the _STRIDE_ is known to be 8 from Table A1, and the anchor belongs to the feature map P3 according to the correspondence between the _STRIDE_ and the feature map. Based on this, the sizes of anchors with the top 100 scores predicted by SMR (trained at three different _SCALE_) on the test set were counted, and the feature map attribution of anchors was also calculated, and the results are shown in Table 5.  
From Table 6, it is clear that these anchors accounted for the vast majority on P5, a small percentage on P6, and 0 on P2, P3, and P4, so it can be assumed that feature maps P2, P3, and P4 did not contribute positively to the prediction results. To explore the effect of feature maps P5 and P6 on inference results, the SMR of the simplified backbone network was trained, and the trained model was tested under the following methods with _SCALE_ taken as 40:  
1. Using feature maps P5 and P6.
2. Using feature map P5 only.
3. Using feature map P6 only.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **IoU** & **\(F1\)** & **Parameters/M** & **Memory/Mb** & **t/ms** \\ \hline SMR-40 & 0.829 & 0.909 & 29.8 & 808.0 & 123.8 \\ SMR-RS & 0.843 & 0.915 & 27.6 & 549.0 & 77.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the performance of the SMR-RS and the SMR-40.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **P2** & **P3** & **P4** & **P5** & **P6** \\ \hline SCALE-8 & 0 & 0 & 0 & 86\% & 14\% \\ SCALE-16 & 0 & 0 & 0 & 72\% & 28\% \\ SCALE-24 & 0 & 0 & 0 & 80\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical results of feature map attribution for anchors.  
Their performance on the test set is shown in Table 6, where it can be seen that the segmentation performance is better in methods 1 and 2, with no degradation compared to the SMR-40, and was slightly improved in method 2 and underperformed in method 3.  
As shown in Figure 9, both method 1 and method 3 perform less well than method 2 in terms of segmentation due to the influence of the feature map P6, which generated lower quality region proposal. Although the region proposal was identical for methods 1 and 3, the lack of semantic information in feature map P6 in method 3 caused a reduction in the quality of the mask.