# A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information
## 5 Conclusions  
In this work, we proposed a lightweight deep encoder-decoder network to learn contextual features at all stages to solve the corrosion segmentation task. Our proposed encoder part consists of a local contextual block and a downsample block that used a mixture of large and small convolution kernels to establish long-range dependencies between pixels and local detailed information. Moreover, we used the core idea of ShuffleNetv2 and a residual connection to reduce the model size and further improve model accuracy. An efficient and simple decoder was proposed to fuse multi-scale features at different stages to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Residual** & **Params** & **mIoU** & **mPA** & **F1-Score** \\ \hline w/o & 2.41 MB & 74.67\% & 85.55\% & 0.821 \\ w & 2.41 MB & **75.64**\% & **86.07**\% & **0.838** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results for residual connection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IRB** & **LCB** & **Params** & **mIoU** & **mPA** & **F1-Score** \\ \hline w & w/o & **2.04 MB** & 74.53\% & 85.31\% & 0.813 \\ w/o & w & 2.41 MB & **75.64**\% & **86.07**\% & **0.838** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation study results for local contextual block.  
augment feature representation capability. With the proposed architecture, we designed two differently sized models and consistently showed excellent corrosion segmentation with a better trade-off between segmentation accuracy and model size. Our proposed small model achieved the best performance for mIoU (75.61%), mPA (86.07%) and F1-score (0.838) with parameters of only 2.41 MB, while the large model remained competitive with 8.25 MB and achieved the best mIoU (79.06%). In future work, we may focus on dataset collection. Through training with larger and more complex datasets, the performance of our model is expected to improve, and a wide range of applications will be covered. Meanwhile, embedding the proposed model into the edge computing chip on site and further optimizing the model constitutes our current work.  
Conceptualization, J.H., Q.L., L.X., G.L. and Y.Z.; Methodology, J.H., Q.L. and L.X.; Funding acquisition, Q.L. and W.C.; Investigation, L.X., G.L. and Y.Z.; Writing code and performing computational experiment, J.H., Q.L. and L.X.; Writing--original draft, J.H., Q.L. and L.X.; Writing--review & editing, J.H., Q.L., L.X., Y.Z. and W.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Beijing Natural Science Foundation (4202026), Qin Xin Talents Cultivation Program of Beijing Information Science and Technology University (QXTCP A202102).  
Not applicable.  
The experimental data that was used in this work can be accessed at [[https://github.com/One-LL/A-Lightweight-Residual-Model-for-Corrosion-Segmentation-with-Local-Contextual-Information](https://github.com/One-LL/A-Lightweight-Residual-Model-for-Corrosion-Segmentation-with-Local-Contextual-Information)]([https://github.com/One-LL/A-Lightweight-Residual-Model-for-Corrosion-Segmentation-with-Local-Contextual-Information](https://github.com/One-LL/A-Lightweight-Residual-Model-for-Corrosion-Segmentation-with-Local-Contextual-Information)), accessed on 10 August 2021.  
The authors declare no conflict of interest.