# Lightweight Stacked Hourglass Network for Human Pose Estimation
## 3 Proposed Method
### Residual Block Design
#### 3.2.3 Proposed Multidilated Light Residual Block  
The original stacked hourglass network constructed an hourglass module using a preactivated residual block, as shown in Figure 5a. The structure of the preactivated residual block is [ReLU\(\rightarrow\)Batch Normalization\(\rightarrow\)Convolution]; this is unlike that of a conventional residual block, which is designed as [Convolution\(\rightarrow\)Batch Normalization\(\rightarrow\)ReLU]. This structure is advantageous in building a deep network and improves the training speed [35]. However, residual blocks were originally designed for image classification or object detection problems (in which it is important to learn local features) where the convolutional receptive field is relatively small. Moreover, in the deep network architecture, although a bottleneck structure is used to reduce the number of parameters and computation cost, the residual block with a bottleneck structure is still large when designing a stacked multistage network, such as an hourglass network. Therefore, there is a need for a residual block with a new structure that can improve performance in human pose estimation by reducing the size of the network and expanding the receptive field. Figure 4: Depthwise separable convolution. Each channel performs convolution using an independent kernel, which is referred to as depthwise convolution. Pointwise convolution is then performed with a \(1\times 1\) kernel. Figure 5: _Cont._In this study, to design a residual block with a new structure capable of solving the aforementioned problems, experiments were performed on residual blocks with various structures. Figure 5b shows a structure in which the middle layer of the preactivated residual block shown in Figure 5a has been changed to a depthwise separable convolution. Using this residual block, we carried out experiments to observe the effect of the depthwise separable convolution on the network size and performance in human pose estimation. Since preactivated residual blocks are bottlenecks with the first and last layers having \(1\times 1\) convolutions, it did not make sense to reduce the number of parameters by changing the layer to a depthwise separable convolution. The researchers in [34] declared that if a nonlinear function is used between a depthwise convolution and a \(1\times 1\) convolution, the performance is significantly reduced. Therefore, all of the depthwise separable convolutions used in this paper consist of a structure that does not use an activation function between the depthwise convolution and \(1\times 1\) convolution, such as [ReLU\(\rightarrow\)Batch Normalization\(\rightarrow\)Depthwise Convolution\(\rightarrow\)\(1\times 1\) Convolution]. To evaluate the effect of the bottleneck structure of residual blocks while using a depthwise separable convolution, we designed the new module shown in Figure 5c. Figure 5c is a modified structure of Figure 5b, where we changed the standard convolutions of the first layer [256\(\rightarrow\)128, \(1\times 1\)] and the last layer [128\(\rightarrow\)256, \(1\times 1\)] into depthwise separable convolutions of [256\(\rightarrow\)128, \(3\times 3\)] and [128\(\rightarrow\)256, \(3\times 3\)], respectively. Figure 5d is our proposed multidilated light residual block, where a residual block of a new structure was used for improving the performance and reducing the number of parameters. Table 1 below shows the detailed structure of the proposed residual block.