## 5 Conclusions  
In this paper, we proposed an efficient atrous residual network, named EAR-Net, that achieved high accuracy while maintaining the computation cost of previous models. First, we aimed to minimize the amount of computation: DSConv was applied to all traditional convolutions, except encoding blocks in the feature extraction process, and interpolation was applied instead of transposed convolution in the feature restoration process. Second, the proposed EAR-Net achieved high accuracy. It improved the context information extraction ability by using residual learning and ASPP in the encoder. Finally, to alleviate the class imbalance between the background and the object, the learning efficiency was improved by utilizing focal loss. The experimental results on the Cityscapes dataset showed high accuracy with a similar amount of computation when compared with the conventional segmentation methods. Through this, EAR-Net can be used in applications, such as autonomous vehicles and machine vision, where both processing  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params (M)** & **MIoU (\%)** \\ \hline EAR-Net w/ traditional conv & 41.0 & 70.6 \\ EAR-Net & 26.8 & 72.3 \\ \hline MIoU: mean intersection over union; Params: number of parameters; EAR-Net: baseline model equal to proposed method; EAR-Net w/ traditional conv: EAR-Net with traditional convolution in Figure 2a. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation study for DSConv analysis on the Cityscapes validation set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Params (M)** & **MIoU (\%)** \\ \hline BS & 24.3 & 67.0 \\ BS + ASPP (rate = 4, 8, 12) & 26.8 & 71.0 \\ BS + ASPP (rate = 6, 12, 18) & 26.8 & 72.3 \\ BS + ASPP (rate = 8, 16, 24) & 26.8 & 71.0 \\ \hline MIoU: mean intersection over union; Params: number of parameters; BS: baseline model without atrous spatial pyramid pooling (ASPP); BS + ASPP: baseline model with ASPP; rates set in atrous convolution are sequentially indicated.  
speed and accuracy are important. Future research is necessary to apply EAR-Net in various fields.  
Conceptualization, S.S. and S.L.; data curation, S.S.; formal analysis, S.S. and H.H.; investigation, S.S.; methodology, S.S. and S.L.; project administration, S.L.; software, S.S. and H.H.; supervision, S.L. and H.H.; validation, S.S. and H.H.; visualization, S.S.; writing--original draft preparation, S.S.; writing--review and editing, S.L. and H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available on [[https://www.cityscapes-dataset.com](https://www.cityscapes-dataset.com)]([https://www.cityscapes-dataset.com](https://www.cityscapes-dataset.com)), accessed on 30 September 2021, reference number [31].  
The present research was conducted by the Research Grant of Kwangwoon University in 2021.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EAR-Net & Efficient atrous residual network \\ DSConv & Depthwise separable convolution \\ ASPP & Atrous spatial pyramid pooling \\ CNN & Convolutional neural networks \\ FCN & Fully convolutional networks \\ BN & Batch normalization \\ ReLU & Rectified linear unit \\ ADSC & Atrous depthwise separable convolution \\ GAP & Global average pooling \\ \end{tabular}