# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 5 Conclusions  
In this paper, we proposed two lightweight models based on U-Net, depthwise separable convolutions, ConvMixer layer, and an inception module to extract road areas from unmanned aerial vehicle imagery. Specifically, the standard \(3\times 3\) convolution operation used in naive inception block was substituted for the ConvMixer layer, which produces the same result for reduced computational cost. In addition to that, we replaced the max-pooling operation in the original inception module with three cascaded ConvMixer layers that produces the effect of one \(7\times 7\) convolution operation. The utilization of the inception module allowed us to learn images on different scales. Furthermore, path networks were incorporated along skip connections to alleviate the semantic gap between encoder and decoder stages. Finally, we substituted the transposed convolution upsampling method in the decoder part of the networks for Bilinear interpolation to overcome the checkerboard artifact issue. We conducted extensive experiments on publicly available Massachusetts roads dataset. The experiments show that the proposed models yielded superior results compared to four state-of-the-art models while containing remarkably fewer parameters. On top of that, the proposed models experienced relatively faster convergence compared to baseline networks, easily achieving 75% accuracy benchmark within only 10 epochs of training.  
Although the proposed models allowed us to decrease the parameter size considerably, performance was negligibly higher than that of the baseline models. We believe that there is still a room for improvement for our models not only in terms of performance but also further reductions in parameter size. One example could be the optimization of channel size distributions within the inception blocks across the models, which is set equally for all channels in this work. In future studies, we will carry on improving our models and investigate their potential by experimenting with various domain datasets.  
Conceptualization and methodology, F.S.; validation and formal analysis, F.S.; resources and data curation, J.-H.P.; writing--original draft preparation, F.S.; investigation, D.-W.L.; writing--review and editing, J.-M.K. and S.Y.; supervision, J.-M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Dongil Culture and Scholarship Foundation.  
Not applicable.  
Massachusetts roads dataset can be found at [[https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)]([https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)) (accessed on 9 January 2022).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned aerial vehicle \\ DL & Deep learning \\ CNN & Convolutional neural networks \\ SVM & Support vector machines \\ MRF & Markov random fields \\ FCN & Fully convolutional network \\ CML & ConvMixer layer \\ BCE & Binary cross entropy \\ mIoU & Mean intersection over union \\ PA & Pixel accuracy \\ TP & True positive \\ FP & False positive \\ FN & False negative \\ \end{tabular}