## 5 Conclusions  
The proposed model-switching architecture prevents mutual suppression between weights in a single CNN model. This architecture uses multiple CNN models to store the weights of various states individually and uses one or more CNN classifiers to identify the current state and switch to a suitable model for semantic segmentation.  
However, the model-switching architecture and semantic segmentation require considerable computation, resulting in time delays. Therefore, we proposed a simple but effective light-weight CNN method to increase the calculation speed. The execution speed was limited by the memory bandwidth in this experiment. Nevertheless, it was approximately two times faster than that of the original method. The proposed method was particularly effective for VGG-like CNNs.  
This paper used FCN for semantic segmentation which is the pioneer in field of semantic segmentation. However, the architecture was limited to the aspect ratio of the input images. Additionally, we plan to conduct bilateral network research on semantic segmentation deep learning in the future.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Day and Night** & **Day** & **Night** & **Avg** & **Model Size** \\ \hline VGG16 & 0.9992 & 0.9980 & & 0.9986 & 1.6 GB \\ LWF-VGG-tiny & 0.9976 & 0.9792 & & 0.9884 & 32 MB \\ LWF-VGG & 0.9989 & 0.9984 & & 0.9986 & 230 MB \\ \hline Weather & Sunny & Cloudy & Rainy & \\ \hline VGG16 & 0.9955 & 0.9981 & 0.969 & 0.9875 & 1.6 GB \\ LWF-VGG-tiny & 0.9940 & 0.9912 & 0.974 & 0.9864 & 32 MB \\ LWF-VGG & 0.9954 & 0.9951 & 0.980 & 0.9902 & 230 MB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of VGG16, LWF-VGG, and LWF-VGG tiny as classifiers. Avg: average.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Multi-Model/** & **Avg IOU** & **Avg REC** & **Avg PRE** &
\begin{tabular}{c} **Avg** \\ **F1-Score** \\ \end{tabular} & **FPS** \\ \hline Single-model VGG16 FCN & 0.853 & 0.844 & 0.862 & 0.861 & 0.89 \\ Single-model LWF-VGG & 0.883 & 0.878 & 0.903 & 0.897 & 2.96 \\ FCN tiny & & & & & \\ Single-model & 0.891 & 0.884 & 0.902 & 0.903 & 1.80 \\ LWF-VGG FCN & & & & & \\ Multi-model & 0.908 & 0.898 & 0.928 & 0.910 & 0.75 \\ VGG16 FCNs & & & & & \\ Multi-model LWF-VGG & 0.937 & 0.953 & 0.947 & 0.947 & 2.51 \\ FCNs tiny & & & & & \\ Multi-model & 0.949 & 0.952 & 0.951 & 0.952 & 1.53 \\ LWF-VGG FCNs & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multi-model and single-model testing using the NTUT mixed dataset. Avg: average; IOU: intersection over union; REC: recall; PRE: precision; FPS: frame per second.  
**Funding:** This research was funded by the Ministry of Science and Technology of Taiwan (MOST 109-2221-E-027-041, MOST 109-2622-E-027-017-CC3).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Our datasets are open-source and available at [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) ch49 Yu2C417 EAOrhBYSEiBDxSQVmEKS?usp=sharing]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) ch49 Yu2C417 EAOrhBYSEiBDxSQVmEKS?usp=sharing) (accessed on 8 August 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.