# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 4 Experiments
### Quality Comparison between Different CNN Backbones  
We performed several experiments to test the ability of different CNN architectures to perform semantic segmentation using the proposed method. We trained three other architectures as feature extractors (VGG16 [10], ResNet50 [11], and DenseNet121 [37]) in addition to Xception and MobileNetV2. All were trained on the NYUV2 segmentation dataset with an image size of \(640\times 480\) for 1000 epochs. We then compared the features and the constructed segmentation masks obtained using the DTS layer (a sample test image is shown in Figure 5). For VGG16 and DenseNet121, the results showed features with inaccurate blurry edges, which were also apparent in the constructed segmentation masks. Better features and constructed masks with far more detail of the object edges and boundaries were extracted in the case of ResNet50 and MobileNetV2. The highest-detail features and the best-constructed segmentation maps were obtained for the Xception architecture. Therefore, we adopted this architecture as the feature extractor of our method. We adopted  
Figure 4: Class definitions and colormaps for the used benchmarks. (**a**) PASCAL VOC2012 [2] class definitions and colormap, (**b**) NYUV2 [3] class definitions and colormap, and (**c**) Cityscapes [4] class definitions and colormap. MobileNetv2 as the feature extractor for DTS-Net-Lite because that architecture exhibited average performance but the fewest multiplication/addition computations (MACs) (0.3 Giga; far lower than those of the other architectures), to achieve higher-speed processing. Table 1 compares the five architectures based on the mean pixel accuracy (Pix.acc.) of each CNN with the DTS layer. This is a measure of the percentage of the correctly classified pixels in the predicted mask. The MAC and parameter numbers are also compared. To further support our selection and the proposed architecture, we trained DTS-Net on DeepGlobe satellite images dataset for road extraction using binary segmentation. We compared the obtained segmentation results from our architectures (using Xception and MobileNetV2 backbones) with ResNet-D2S and VGG16-D2S proposed in [28] (we also used Segnet results on DeepGlobe as the authors of this paper [28] trained SegNet [6] on DeepGlobe and added the results in their paper) in Figure 6. The comparison is simply a quality comparison of samples from the DeepGlobe validation satellite images as the validation server for this challenge is closed and there is no other way to validate the results. Our results on the binary segmentation of the satellite images show better quality in extracting more details than the results produced by ResNet-50 [11] and VGG16 [10] backbones in [28], even better than SegNet [6], which proves the excellent performance of the proposed architectures using Xception and MobileNetV2 backbones. Figure 5: Quality comparison of (**a**) extracted feature maps before the DTS layer and (**b**) constructed segmentation mask using DTS layer (without NLF) between VGG16, ResNet50, DenseNet121, MobileNetV2, and Xception architectures. Figure 6: Quality comparison of the binary segmentation task on DeepGlobe [29] dataset between the models proposed by [PERSON] et al. [28] (ResNet-D2S and VGG16-D2S) and SegNet [6] in the same paper and our models: DTS-Net and DTS-Net-Lite which uses Xception and MobileNetV2 as backbones, respectively. We selected depth-wise separable convolution as being suitable for the proposed DTS module based on the following main reason. The DTS layer constructs the final image using a pixel reordering technique through the channel axis to form super-pixels in the segmentation map, which highly depend on the feature-map quality. Depth-wise convolution extracts the features from each channel separately and then projects them using point-wise convolution. This approach extracts more accurate feature maps as the depth-wise convolution focuses on the data in each channel separately.