# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Experiment and Analysis
#### 4.4.4 Image Segmentation Results  
Using the same training environment and parameters, we conducted comparative experiments on different semantic segmentation networks. Figure 12 illustrates the prediction results of various networks. From top to bottom are the results of segmenting different types of images using FCN, HRNet18, U-Net, DeepLabv3+, and our proposed method, with the last row showing the annotated ground truth images. We chose to compare our method with FCN, HRNet18, U-Net, and DeepLabv3+, which are some of the most outstanding methods in the field of semantic segmentation, demonstrating excellent performance on various datasets. These methods represent different characteristics: FCN transforms traditional convolutional networks into fully convolutional structures for segmentation tasks; U-Net addresses the issue of information loss in semantic segmentation by introducing skip connections; DeepLabv3+ enhances segmentation performance through dilated convolutions and multi-scale feature fusion; and HRNet improves segmentation accuracy by preserving high-resolution features. By comparing these representative methods, we can gain a comprehensive understanding of the strengths and limitations of different segmentation approaches. Our model demonstrates superior segmentation results across the six object categories requiring segmentation, with masks that are smoother and more complete. It is observable that in the recognition of lawns, boundaries, and humans, the performances of these algorithms appear quite similar. However, in the recognition of tree trunks, although the FCN and U-Net networks can correctly identify tree trunks, their ability to recognize lawns is significantly lacking, which is undesirable. In the recognition of obstacles, the algorithm proposed in this paper produced results closest to the ground truth images, with other algorithms missing some details. In recognizing shrubs, especially in the hard-to-recognize area on the lower right side of the shrubs, our algorithm's recognition effect is visibly better than that of the other algorithms. Overall, as shown in Table 6, our method achieves segmentation results that are nearly identical to those of the computationally expensive DeepLabV3+ architecture, with a reduction in both the number of parameters and computational cost in GFLOPs. This lays a solid foundation for our model's future deployment in embedded systems, making it suitable for operation on machines with limited resources. As observed in Table 7, the network model proposed in this study was compared with other types of networks. Clearly, our network model far surpasses the other network models in terms of FPS, approaching 30. The closer the FPS value is to 30, the better the compatibility of the model with the ORB-SLAM3 algorithm. In terms of the MIoU and MPA, which are indicators of model recognition accuracy, our proposed network forms the best, exceeding the recognition effects of other networks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MIoU/\%** & **MPA/\%** & **GFLOPs** & **Params** & **FPS** \\ \hline Baseline & 92.28 & 95.52 & 176.52 & 39.31 & 14.86 \\ Baseline + A & 92.86 & 96.13 & 176.53 & 40.87 & 14.85 \\ Baseline + B & 90.55 & 94.66 & 68.24 & 14.32 & 28.39 \\ Baseline + B + A & 91.07 & 94.71 & 68.25 & 15.10 & 27.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment results. Although our architecture does not appear to be lighter than HRNet18 in terms of GFLOPs and Params, as shown in Table 7, compared to HRNet18, our method only demonstrates decreases of 1.03% and 0.43% in the MIoU and MPA, respectively. Relatively speaking, although HRNet18 is lighter, it has a lower degree of recognition accuracy and cannot achieve satisfactory results. Moreover, even though it has a lower number of parameters, its FPS improvement is not significant, showing no advantage over our proposed architecture.