# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Semantic Segmentation Module
#### 3.1.3 Lightweight Backbone Network MobileNetV2  
MobileNetV2 is a lightweight neural network model proposed by the Google team in 2018. Compared to the traditional residual structure of ResNet, MobileNetV2 has fewer parameters, a simpler model structure, and a faster network training speed.  
The MobileNet architecture introduces depthwise separable convolution to replace standard convolution, reducing computational complexity and model parameters. Depthwise separable convolution decomposes a regular convolution into depthwise convolution and pointwise convolution. It first decomposes the input features into multiple single-channel features and applies a 3 \(\times\) 3 convolution kernel on each channel, which is known as depthwise convolution. Then, pointwise convolution convolves the result of the depthwise convolution with a 1 \(\times\) 1 convolution, assembling the output features. Depthwise separable convolution can significantly reduce computational complexity compared to standard convolution with a relatively modest decrease in prediction accuracy.  
Building upon depthwise separable convolution, MobileNetV2 introduces an inverted residual structure and employs a linear layer in the final layer, further enhancing network performance. The inverted residual structure consists of three main parts. First, it increases the dimension of input features using a 1 \(\times\) 1 convolution. Then, it extracts features using a 3 \(\times\) 3 depthwise separable convolution, followed by a dimension reduction using a 1 \(\times\) 1 convolution. Unlike the residual structure, the inverted residual structure avoids the drawback of the network performing well only on low-dimensional features when the input feature channel count is low, leading to the loss of high-dimensional feature information. To prevent the destruction of features by the ReLU function, the inverted residual structure employs 1 \(\times\) 1 convolutions for dimensionality expansion before the 3 \(\times\) 3 network structure and for dimensionality reduction after the 3 \(\times\) 3 network structure. The Rectified Linear Unit (ReLU) activation function is replaced with a linear function to minimize the loss of useful network information. The network architecture of MobileNetV2 is shown in Table 1.  
Figure 2: Improving the DeepLabv3+ architecture.  
In Table 1, \"Input\" represents the size of the input feature map for the current layer. \"Network\" shows the network structure of each layer in MobileNetV2, including regular convolution layers, inverted residual structures, and average pooling layers. \"t\" is the expansion factor for channels in the inverted residual structure, \"n\" is the repetition count for the current layer, \"c\" is the number of output channels, and \"s\" is the convolution stride for the current layer.  
The method proposed in this paper ultimately needs to ensure that the neural network model can be invoked in ORB-SLAM3; in practical applications, it must be deployed on embedded devices. Typically, algorithms running on embedded devices have a faster recognition speed and utilize less storage space while achieving approximate recognition accuracy. MobileNetV2 is a lightweight network suitable for embedding in mobile devices. Compared to the ResNet series network originally used as the backbone feature extraction network in DeepLabv3+, MobileNetV2 is better suited for deployment on edge devices, offering more efficient parameters and a faster speed.