# SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
## 3 Results and Analysis
### Optimization Experiments for SMR  
The segmentation performance of the original SMR on the test set is shown in Table 2. Compared with Mask R-CNN, the number of parameters of SMR was reduced significantly, from 43.7 M to 29.8 M, a reduction of 31.8%, and the GPU memory consumption was reduced by 6.3% prediction speed was increased by 6.2%, the training loss is slightly smaller, as in Figure 5, but the segmentation mask quality was significantly reduced. To solve this problem, SMR was optimized for experiments, and the segmentation performance of SMR after optimization even surpassed that of Mask R-CNN, and this optimization trial process is described below. To explain the specific reasons for the poor segmentation performance of the original SMR, the prediction results of the region proposals in the test set are shown in Figure 6; each of these images shows the top 100 region proposals (before NMS). It is clear that although the region proposals were above the row of rolled rice stubble, it is so small that it only covered a small part of the row. According to Section 2.2.2, the size of the region proposals can be changed by modifying the hyperparameters _SCALE_ and _RATIO_. In the original SMR, the values of _SCALE_ and _RATIO_ are consistent with Mask R-CNN, which are _SCALE_ = 8 and _RATIO_ = {0.5, 1, 2} [47]. On this basis, the _SCALE_ value of SMR was made to increase from 8 to 16, 24, 32, 40, and 48 at the same time as _RATIO_ varies by a factor of 0.2, 1, and 5 during the process of inference. The IoU performance of SMR and Mask R-CNN under simultaneous changes of _SCALE_ and _RATIO_ is shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & _IoU_ & _F1_ & **Parameters/M** & **Memory/Mb** & _t/ms_ \\ \hline Mask R-CNN & 0.826 & 0.906 & 43.7 & 862.0 & 132.0 \\ SMR & 0.126 & 0.217 & 29.8 & 808.0 & 123.8 \\ SMR-40 & 0.829 & 0.909 & 29.8 & 808.0 & 123.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SMR performance before and after optimization. Figure 6: Region proposals generated by the original SMR. The white dot in the images indicates the midpoint of the region proposals. (1): original image; (2): region proposals on the original image; Three different rolled rice stubble rows: (**a**) heavily straw-covered; (**b**) yellowish; (**c**) greenish. From Figure 7 for the Mask R-CNN, changes in _SCALE_ and _RATIO_ barely improve IoU performance and bring it down. However, for SMR, as the value of _SCALE_ increased from 8 to 32, the IoU gradually increased and continued to increase from 32 to 48; the IoU did not increase significantly. In contrast, the change in the value of _RATIO_ has a less strong effect on the IoU, but there was still a certain pattern to follow, with all _SCALE_ conditions having higher values of IoU at 1.0\(\times\), i.e., at the original _RATIO_. The performance of the mask at _SCALE_ = 40 and _RATIO_ = {0.5, 1, 2} is shown in Table 2 in row SMR-40, which indicates that the quality of the mask exceeds that of Mask R-CNN. According to the above experimental results, the reason for the poor quality of the original SMR segmentation mask is that the _SCALE_ is too small, and using a larger value for prediction can effectively improve the quality of the mask; changing the _RATIO_ to deviate from the original would cause to a decrease. In order to investigate the performance of SMR under different values of _SCALE_ of training, the SMR was trained using _SCALE_ = 16 and 24, respectively (the reason for not continuing to increase the _SCALE_ value is that the model converges slowly in the training process after further increase), and then adjusting the _SCALE_ value in the prediction process to optimize the mask quality.