# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 3 Proposed Method
### Feature Extractor Architectures  
The feature extractor is the most important part of a dense prediction CNN model, as the dense predictions are highly dependent on the extracted features. Depth-wise separable convolution was first presented by [PERSON] [14] and was clearly shown to be much faster and more efficient than normal convolution. Depth-wise separable convolution-based architectures (such as Xception and MobileNetV2 [36]) can attain high accuracy for ImageNet classification and other tasks (such as object detection and semantic segmentation) with a significantly low parameter number and low floating-point operations count compared with conventional CNN-based architectures. **Xception**[14] was also proposed by [PERSON]. This unique convolution approach consists of two main modules: depth-wise and point-wise convolution. In depth-wise convolution, the convolution is performed for each channel separately, whereas point-wise convolution involves a \(1\times 1\) convolution through the channels; the latter can be regarded as channel projection or dot-product multiplication between channels. As regards the computational cost of depth-wise separable convolution [36], it is clearly apparent that this convolution has \(\frac{1}{N}+\frac{1}{D_{k}^{2}}\) fewer parameters than conventional convolution, where \(N\) is the number of channels and \(D_{k}\) is the kernel size. The original Xception architecture proposed for Imagenet classification consists of three stages. The main building block of the model is the Xception block, which involves two or three depth-wise separable convolutions with a kernel size of 3 \(\times\) 3 and rectified linear unit (ReLU) activation with or without max-pooling at the end. Residual connections are used to connect the block from start to end. The first stage involves the entry flow, which has three Xception blocks with max-pooling at the end of each block. The second stage involves the middle flow, which has eight Xception blocks without max-pooling. Finally, the third stage pertains to the exit flow, which has one Xception block with max-pooling and then two depth-wise separable convolutions. The Xception block details are shown in Figure 3a. The final feature map depth is 2048. In our proposed approach, we then add a 1 \(\times\) 1 \(\times\) 1024 (i.e., 32 \(\times\) 32) convolution to reduce the depth from 2048 to 1024, so as to aggregate a segmentation map that is 32 times the width and 32 times the height of the final feature maps after the DTS layer. As a second branch, we add global average pooling and then a fully connected layer with sigmoid activation to obtain the multi-label classification predictions. The Xception feature extractor is incorporated in the main network proposed in this work, that is, DTS-Net. **MobileNetV2**[36] is the other architecture employed in this study and is implemented in DTS-Net-Lite to accelerate our approach for real-time application on low-computational-power devices such as embedded devices or mobile phones. Like Xception, MobileNetV2 adopts depth-wise separable convolution as the main building module, to benefit from the cheap computational cost of this module along with other features that further decrease the computation. In MobileNetV2, inverted residuals and linear bottlenecks are implemented. The feature maps can be encoded in a low-dimensional subspace using the bottlenecks with a linear operation. A bottleneck consists of an expansion module (1 \(\times\) 1 convolution with a higher number of filters with expansion factor \(t\)), followed by depth-wise separable convolution with a 3 \(\times\) 3 kernel and, finally, a projection module (1 \(\times\) 1 convolution with a  
Figure 2: The architecture of the proposed method. With an Xception (DTS-NET) or MobileNetV2 (DTS-Net-Lite) backbone, the DTS layer (module) is added after a 1 \(\times\) 1 \(\times\) \(r^{2}\) convolutional layer. The pixels in the \(h\times w\times r^{2}\) low-resolution feature maps are arranged in an \(rh\times rw\) segmentation map.