# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 6 Conclusions  
In this paper, we propose a novel nested u-shaped residual codec network (NURCNet) to improve conventional convolutional neural networks (CNNs) for the industrial defect detection problem. The embedding of the dilated convolution, attention mechanism, and fusion loss ensures that NURCNet can capture abundant details without increasing the calculation amount too much. In the encoder, we use a fully convolutional neural network and attention mechanism to extract both the rich low-level spatial details and high-level contextual information. Then, to aggregate multiscale deep features, we utilize the U-shaped decoder to progressively integrate deep features in a top-down way. the encoder and decoder forms a nested U-shaped residual codec network. Finally, a residual refinement network is introduced to further optimize the coarse saliency map which is output by the encoder-decoder stage. Experimental results show that, compared to the eight state-of-the-art models, our model has the best performance in the industrial defect detection problem. In addition, our NURCNet model does not require any postprocessing.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **PCRR** & **2 LSG** & **BC** & **SMD** & **PoolNet** & **PiCANet** & **CPD** & **BASNet** & **Ours** \\ \hline Code & M & M & M + C & M + C & P & P & P & P \\ Size & - & - & - & - & 260 & 180 & 183 & 332 & 263 \\ Time & 1.095 & 0.639 & 0.054 & 0.319 & 0.030 & 0.116 & 0.055 & 0.046 & 0.035 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the model size and the average running time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MAE** & **WF** & **SM** & **PFOM** \\ \hline PCRR & 0.2552 & 0.2557 & 0.5302 & 0.3138 \\
2 LSG & 0.2587 & 0.3007 & 0.5368 & 0.3530 \\ BC & 0.1519 & 0.3733 & 0.5881 & 0.3352 \\ SMD & 0.1994 & 0.3613 & 0.5840 & 0.3748 \\ PoolNet & 0.0345 & 0.7263 & 0.8213 & 0.7060 \\ PiCANet & 0.0351 & 0.7521 & 0.8490 & 0.7547 \\ CPD & 0.0353 & 0.7235 & 0.8308 & 0.7343 \\ BASNet & 0.0160 & 0.9033 & 0.9235 & 0.8880 \\ NURCNet & **0.0139** & **0.9137** & **0.9511** & **0.9065** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results from nine competitive methods.  
**Author Contributions:** Conceptualization, H.G., S.Z., L.Z., W.Z., Y.S. and J.W.; methodology, H.G., S.Z., L.Z. and Y.S.; software, H.G. and S.Z.; validation, H.G., S.Z. and L.Z.; formal analysis, H.G., S.Z., L.Z., W.Z., Y.S. and J.W.; investigation, H.G., S.Z., L.Z., W.Z. and Y.S.; resources, H.G., L.Z.; data curation, H.G., S.Z., L.Z., Y.S. and J.W.; writing--original draft preparation, H.G., S.Z. and L.Z.; writing--review and editing, H.G.; visualization, H.G. and S.Z.; supervision, H.G., S.Z. and L.Z.; project administration, H.G., S.Z., L.Z. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** I wish to thank the anonymous editor and the reviewers for their constructive comments and recommendations, which have significantly improved the presentation of this paper. This work is in part supported by [National Natural Science Found of China], grant no. [31900710], [Science and Technology Research key Project of the Education Department of Henan Province], grant no. [22A520008] and [Xinyang Normal University Graduate Research Innovation Fund], grant no. [2021 KY10], [Natural Science Foundation of Henan Province], grant no. [222300420275, 222300420274].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.