## 2 Simultaneous Segmentation and Detection Network (SSADNet)
### Simultaneous Segmentation and Detection Network (SSADNet)
#### 2.2.1 Architecture of SSADNet  
The basic architecture of SSADNet is shown in Figure 4. SSADNet has two branched networks, i.e., the segmentation branch and detection branch, and a bridge part connecting the two branches. As opposed to other general CNN-based networks, SSADNet is applied with a fully convoluted layer structure [13] instead of a fully connected layer. A fully convoluted layer structure can have fewer parameters and a simpler model structure than a fully connected layer, thus allowing quick training and inference. Moreover, SSADNet is applied with a dilated convolution filter [25] instead of a regular convolution filter to create a large receptive field with few parameters. Spatial dropout [26] was used in place of regular dropout [27; 28]. Hence, the limitation of having dropouts with insignificant effects in a convolution layer was overcome, allowing us to maintain the regional features and prevent overfitting through generalization (which is the purpose of dropout). Moreover, an exp  
Figure 4: Architecture of Simultaneous Segmentation and Detection Network. Figure 3: An example of a point cloud top view image created. was applied as an activation function in which the slope did not reach 0 even with negative input, thus preventing the slope from plateauing. The ELU activation function is as shown in Equation (2). \[ \begin{array}{ll}f(x)=x&\text{if }x>0\\ f(x)=\alpha(\text{e}^{x}-1)&\text{if }x\leq 0\end{array}  \tag{2}\]  
A. Segmentation Branch  
The segmentation branch in SSADNet for segmentation training and inference consists of four parts: the encoder-context module-decoder, and the fully convolutional layers for pixel classifiers. The structure of the segmentation branch was expanded and transformed to the LoDNN [24] structure so it could detect various types of vehicles and drivable road areas. Figure 4 shows the composition of a segmentation branch, and Figure 5 shows the detailed structure of a segmentation branch. The encoder in a segmentation branch compresses the input point cloud top view image, and extracts features. The context module was used to extract features from the compressed feature map to train it to decide which class each pixel belonged to. The decoder reconverted the compressed feature map to its original size and restored the location information. In the context module, a \(3\times 3\) dilated convolution filter having various receptive field sizes was applied using various dilation ratios (as shown in Table 1 and Figure 5) to extract information on drivable areas and the locations of vehicles. Furthermore, spatial dropout was used to maintain the extracted regional features while preventing overfitting. A pooling layer was applied several times, which simplified the structure of the encoder and decoder since larger receptive fields were obtained using a dilated convolution filter. The context module was used in between the simplified encoder-decoder structure to extract features. A softmax transfer function, which is a pixel classifier connected to the last layer, was used to perform classification. Unlike other previous semantic segmentation networks based on fully convolutional networks, and comprising an encoder-decoder structure, our proposed segmentation branch of SSADNet applies a context module between the encoder and decoder. The dilated convolution filter is to expand the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Context Module** & **Dilation Rate (Width, Height)** & **Receptive Field** \\ \hline
**1** & \((1,1)\) & \(3\times 3\) \\ \hline
**2** & \((1,2)\) & \(5\times 7\) \\ \hline
**3** & \((2,4)\) & \(9\times 15\) \\ \hline
**4** & \((4,8)\) & \(17\times 31\) \\ \hline
**5** & \((8,16)\) & \(33\times 63\) \\ \hline
**6** & \((16,32)\) & \(65\times 127\) \\ \hline
**7** & \((32,64)\) & \(129\times 255\) \\ \hline
**8** & - & \(129\times 255\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Context module of segmentation branch. Figure 5: Detailed structure of segmentation branch.