# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Evaluation Indicators  
To objectively assess the semantic segmentation model's performance on the lawn segmentation dataset and facilitate comparisons with various methods, the following evaluation metrics were adopted: the MIoU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy). The MIoU is widely used to measure pixel-level overlap between model-predicted segmentation results and actual labels. It calculates the intersection of predicted values and actual values for individual pixel classes. The MIoU reflects a model's ability to accurately segment image pixels by averaging the IoUs for all categories. In lawn segmentation tasks, a higher MIoU indicates the model's ability to accurately capture the boundaries and shapes of objects in the lawn environment. The formula is as follows (2):  
\[MIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{FN+FP+TP} \tag{2}\]  
The CPA (Category Pixel Accuracy) measures how many pixels predicted to be positive by the model are true positives. The calculation formula is as follows (3):  
\[class\;i:P_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}^{\prime}} \tag{3}\]  
The MPA (Mean Pixel Accuracy) calculates the proportion of correctly classified pixels in each class, i.e., the CPA, and then computes the average through accumulation, as shown in Formula (4):  
\[MPA=\frac{sum(P_{i})}{number\;of\;classes} \tag{4}\]  
The calculation of these two parameters is carried out through a pixel-level comparison between the model's predicted results and the actual labels. These metrics provide robust support for objectively evaluating the performance of semantic segmentation models. Here, the symbol \"\(k\)\" represents the number of segmentation classes, and the symbol \"\(i\)\" represents the predicted category. True Positive (TP), False Negative (FP), True Negative (TN), and False Positive (FN) parameters are used, and this series of parameters forms a solid foundation for the in-depth analysis of the performance of semantic segmentation models. In this study, the backbone network was replaced, and the original model underwent lightweight processing. Therefore, it was necessary to evaluate the model's size and running speed to verify the effectiveness of the improvements. We used metrics such as Giga Floating-Point Operations Per Second (GFLOPs), Params, and FPS for a comprehensive comparison. Floating-Point Operations Per Second (FLOPs) is a critical metric for measuring the computational complexity and performance of neural network models. It represents the \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Leaning Rate & 0.01 \\ Weight Decay & 0.0005 \\ Momentum & 0.9 \\ Epoch & 140,000 \\ Batch Size & 4 \\ Number of Classes & 7 \\ Crop Size & \(512\times 512\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DeepLabv3+ training parameters. number of floating-point operations performed per second. In neural networks, floating-point operations involve floating-point arithmetic operations on weights, inputs, and activation values. GFLOPs, indicating billions of floating-point operations per second, is a key indicator for assessing model complexity. For resource-constrained devices such as mobile or edge devices, lower GFLOPs values may be more suitable to ensure the model runs smoothly on these devices. Params refers to the number of parameters that the model needs to learn. These parameters include weights and bias terms which are adjusted during the model's training process to enable it to learn from input data and produce appropriate outputs. This is also an important metric for evaluating the complexity of a neural network model. The FPS metric is used to evaluate detection speed, indicating the number of images processed per second or the time required to process a single image. A shorter time implies a higher speed. In this paper, we use the FPS metric to objectively evaluate the actual running speed of the proposed model.