# SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
## 3 Results and Analysis
### Comparison of the Performance of Different Models  
The instance segmentation models SOLOv2 [52] and Mask Scoring R-CNN [53], YOLACT [54], SMR-RS with MobileNetv3-Large [55] as the backbone network feature extractor (SMR-RS-MN3) and semantic segmentation model Unet [32] were trained according to the parameter settings in Table 1, training losses are shown in Figure 10, and the results obtained for each model on the test set are shown in Table 8. SMR-RS slightly outperforms SOLOv2, YOLACT, and Mask Scoring R-CNN in terms of segmentation performance and has a significant advantage in terms of total parameters, GPU memory occupation, Flops, and running speed, outperforming SOLOv2 by 40.3%, 38.5%, 58.7% and 48.4%, YOLACT by 21.8%, 5.5%, 56.2% and 39.4% and Mask Scoring R-CNN by 37.4%, 41.1%, 56.9%, and 44.0%. Due to its lightweight backbone, the SMR-RS-MN3 has a further reduction in running time consumption and hardware resource consumption from the SMR-RS. However, this also resulted in a reduction in the quality of the segmentation. The performance of the SMR-RS-MN3 demonstrates the scalability of the SMR-RS. Although Unet has a significant inference speed advantage at lower resolutions, it has higher hardware requirements, consumes far more GPU memory and computational power (Flops), and is inferior to other models in segmentation accuracy.  
As shown in Figure 11, the mask prediction results of the proposed model in this paper are compared with those of the state-of-the-art model; there are four representative images, as shown in (a), (b), (c), and (d). As shown in (d), for these two complexes rolled rice stubble rows, only SMR-RS and YOLACT identified all of them, but YOLOCT misidentified a non-rice stubble row, and as shown in (b); SMR-RS was insensitive to a small area of rice stubble rows. For these four images, SMR-RS-MN3 performed well overall, but for the  
Figure 10: Loss curves for the five models. MS R-CNN indicates Mask Scoring R-CNN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _IoU_ & **F1** & **Parameters/M** & **Memory/Mb** & **Flops 1** & **t/ms** \\ \hline SMR-RS & 0.843 & 0.915 & 27.6 & 549.0 & 1.76 \(\times\) 10\({}^{12}\) & 77.4 \\ SMR-RS-MN3 & 0.834 & 0.910 & 6.7 & 338.0 & 1.32 \(\times\) 10\({}^{12}\) & 23.5 \\ Mask R-CNN & 0.826 & 0.906 & 43.7 & 862.0 & 4.08 \(\times\) 10\({}^{12}\) & 132.0 \\ SOLOv2 & 0.839 & 0.915 & 46.2 & 893.0 & 4.26 \(\times\) 10\({}^{12}\) & 150.0 \\ Mask SR & 0.827 & 0.907 & 44.1 & 932.0 & 4.08 \(\times\) 10\({}^{12}\) & 139.9 \\ YOLACT & 0.829 & 0.909 & 35.3 & 581.0 & 4.02 \(\times\) 10\({}^{12}\) & 127.8 \\ Unet 2 & 0.790 & 0.885 & 29.0 & 2491.0 & 8.09 \(\times\) 10\({}^{12}\) & 13.9 \\ \hline \hline \end{tabular}  
* The number of floating-point operations required to infer a single image was calculated using a Python library called thor. 2 The input image is cropped to 1024 \(\times\) 1024 to meet the model’s requirements for resolution.  
\end{table}
Table 8: Performance of seven models.  
stable rows that were heavily straw-covered in (c), it identified a small area of unrolled rice stubble rows as rolled rice stubble rows; SOLOv2 also performed well, but the edge segmentation of rolled rice stubble rows is poor, as shown in (a), (c); Mask Scoring R-CNN has a serious under-recognition phenomenon, and the large area of rice stubble rows located in the center of (d) is not recognized; Mask R-CNN has the phenomenon of repeated recognition, as shown in (b), (d); Unet only achieves semantic segmentation, and there are multiple small regions of false segmentation in these results.