# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 5 Results
### Comparison with SOTA Semantic Segmentation Methods  
We compared the proposed method with SOTA methods in terms of semantic segmentation for the three benchmarks. On PASCAL VOC2012, the proposed method outperformed the recent SOTA methods in terms of mIOU. In detail, the best-performing SOTA method, that is, EfficientNet-L2+NAS-FPN [21] (based on EfficientNetB7, neural architecture search, and a features pyramid network [38]) achieved an mIOU of 90.0%, whereas DTS-Net achieved 91.1%. Table 3 compares the proposed method and the SOTA methods on the PASCAL VOC2012 validation set in terms of mIOU, and indicates the backbone architecture of each method. Further, Table 4 compares the proposed method with the SOTA methods with regard to semantic segmentation on the NYUV2 benchmark in terms of Pix.acc. Our method DTS-NET+NLF outperformed the best SOTA method on NYUV2 13 classes (BPNET [39]) by 11.2%, and DTS-NET-Lite+NLF achieves approximately the same performance as BPNET with 0.2% lower pixel accuracy. We also compared our method with the SOTA methods for semantic segmentation on the CITYSCAPES validation benchmark. As detailed in Table 5, the proposed method outperformed the best SOTA method in terms of mIOU by 0.15%. These results prove that the proposed method as a learning technique exhibits higher performance than the recent SOTA encoder-decoder architectures i.e., Deeplabv3, DeepLabv3+ which also depends on depthwise separable convolution architectures (Xception and MobileNet encoders). We also compared the speed of each model (the speeds are copied from the original papers) in Table 5 while the speed comparison is not so informative since the hardware used for testing and the input image size highly affect the speed of the model, and each method uses different hardware specifications and image size. In general, the proposed method exhibited distinctive performance primarily because of the DTS layer which constructs the initial segmentation result and, secondarily, the NLF filtration which enhances this segmentation result. Figure 9 shows a quality comparison between the semantic segmentation results obtained from the best architecture of the proposed method (DTS-Net+NLF) and those of the SOTA methods which have encoder-decoder architecture and some of them have Xception architecture encoder, that is, DeepLabv3 and DeepLabv3+, on PASCAL VOC2012 and CITYSCAPES benchmarks. In Figure 9, the comparison on PASCAL VOC2012 shows that our method can predict a precise segmentation mask even the unlabeled areas (white pixels in the groundtruth) are labeled as background, noting that the borders of our results have few mislabeled pixels. Those pixels exist due to the use of an image reconstruction method (DTS) to construct the segmentation mask, and the NLF algorithm sometimes fails to correct those pixels as their label is a true label of the segmentation output. On a CITYSCAPES quality comparison in Figure 9, our methods can predict high-quality masks with few noisy borders due to the same reasons mentioned at the previous sentence. Note that the black regions contain many mislabeled pixels and this situation is acceptable as the black pixels are unlabeled pixels that are not counted in the segmentation evaluation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **Pix.acc.\%** \\ \hline SceneNet [43] & VGG16 & 52.5 \\ Hermans et al. [44] & RDF+CRF & 54.3 \\ SemanticFusion [45] & VGG16 & 59.2 \\ ScanNet [46] & NiN [47] & 60.7 \\
3 DMV [48] & Enet [49] & 71.2 \\ BPNet [39] & U-Net & 73.5 \\ \hline
**DTS-Net-Lite+NLF** & MobileNetV2+DTS & 73.3 \\
**DTS-Net+NLF** & Xception+DTS & **84.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the proposed method and SOTA methods on the NYUV2 semantic segmentation test benchmark. Note that ScanNet was evaluated on 11 classes only.