# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 5 Experiments and Discussion
### Comparison Results and Discussion  
The proposed NURCNet is compared with eight of the conventional or deep learning saliency detection methods, i.e., RCRR [48], 2 LSG [49], BC [50], SMD [19], PoolNet [23], PiCANet [51], CPD [25], and BASNet [36]. Table 3 presents the corresponding results of the nine methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structural** & **MAE** & **WF** & **SM** \\ \hline Baseline (B) & 0.0162 & 0.9059 & 0.9244 \\ B + attention & 0.0153 & 0.9048 & 0.9258 \\ B + URB & 0.0140 & 0.9182 & 0.9310 \\ B + URB + RN\_1D & 0.0139 & 0.9223 & 0.9377 \\ NURCNet-URB\({}^{*}\) & 0.0143 & 0.9154 & 0.9321 \\ NURCNet-RN\_1D\({}^{*}\) & 0.0146 & 0.9139 & 0.9320 \\ NURCNet & **0.0120** & **0.9350** & **0.9378** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of using different key components.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Loss** & **MAE** & **WF** & **SM** \\ \hline \(\mathcal{L}_{bce}\) & 0.0141 & 0.9120 & 0.9325 \\ \(\mathcal{L}_{iou}\) & 0.0145 & 0.9110 & 0.9251 \\ \(\mathcal{L}_{ssim}\) & 0.0151 & 0.9001 & 0.9231 \\ \(\mathcal{L}_{bce}+\mathcal{L}_{iou}\) & 0.0142 & 0.9160 & 0.9324 \\ \(\mathcal{L}_{bce}+\mathcal{L}_{ssim}\) & 0.0144 & 0.9082 & 0.9285 \\ \(\mathcal{L}_{all}\) & **0.0120** & **0.9350** & **0.9378** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of different losses.  
Figure 8: Visualization of the results of different losses. (**a**) Strip steel surface images; (**b**) ground truth; (**c**) \(\mathcal{L}_{bce}\); (**d**) \(\mathcal{L}_{iou}\); (**e**) \(\mathcal{L}_{ssim}\); (**f**) \(\mathcal{L}_{bce}\) + \(\mathcal{L}_{ssim}\); (**g**) \(\mathcal{L}_{bce}\) + \(\mathcal{L}_{iou}\); and (**h**) \(\mathcal{L}_{all}\) (ours). Red boxes indicate areas where defects are incomplete.  
From Table 3, we can find that NURCNet achieves excellent performance on four evaluation metrics, i.e., MAE, WF, SM, and PFOM. Specifically, compared to RCRR, 2 LSG, BC, SMD, PoolNet, PiCANet, CPD, and BASNet, NURCNet reduces MAE by 94.5%, 94.6%, 90.8%, 93%, 59.7%, 60.4%, 60.6%, and 13.1%, respectively, and improves WF (SM) by 257.3%, 203.8%, 144.7%, 152.9%, 25.8%, 21.5%, 26.3%, and 1.2% (79.4%, 77.2%, 61.7%, 62.9%, 15.8%, 12.0%, 14.5%, and 3%), respectively. Furthermore, we observe from Table 3 that NURCNet achieves more accurate identification of defect contours. Specifically, NURCNet improves PFOM by 188.9%, 156.8%, 170.4%, 41.9%, 28.4%, 20.1%, 23.5%, and 2.1% when compared to RCRR, 2 LSG, BC, SMD, PoolNet, PiCANet, CPD, and BASNet, respectively. These results indicate that the performance of the NURCNet is better than the eight state-of-the-art models. Therefore, our model will be a better choice in industrial defect detection applications.  
Table 4 records the comparison of the model size (MB) and the average running time (seconds per image) on the SD-Saliency-900 dataset. In Table 4, \"M\" presents that the code is written in MATLAB, \"C\" means that the code is written in CAFFE, and \"P\" denotes that the code is written in PYTORCH. It can be found that our model only needs 0.037 s to detect a 200 \(\times\) 200 image, which makes our model stand out among all models. In real-world industrial defect detection, lightweight models are highly sought after by factories. However, our model size is slightly large when compared with the other models. Therefore, in future work, we will adopt some lightweight techniques to reduce the size of our model.