# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 4 Results
### Reconstruction Performance Evaluation  
To evaluate the classification performance of the proposed method in eddy current signal-based defect detection, we compare the confusion matrices of the proposed CV-CNN and CV-FCNN, as shown in Figure 10 and Figure 11, respectively. These matrices provide visual insights into the predictive accuracy and inter-class confusion behavior across different defect types. The proposed CV-CNN achieved an overall classification accuracy of 85.0%, outperforming CV-FCNN, which attained 82.8%. This 2.2 percentage point improvement demonstrates the superiority of CV-CNN in distinguishing defect patterns from eddy current signals. A deeper analysis of the confusion matrices reveals that CV-CNN significantly suppresses inter-class confusion, as evidenced by the generally higher diagonal elements and more concentrated misclassifications. In contrast, CV-FCNN exhibits greater dispersion in off-diagonal entries, especially among similar defect types. This performance gap is primarily due to the difference in network structure. The fully connected layers of CV-FCNN tend to oversmooth local features, making it more susceptible to confusion between neighboring classes. In contrast, the convolutional structure of CV-CNN effectively preserves spatial-temporal locality within the signal, capturing subtle but critical features of defect signatures. In conclusion, the confusion matrix analysis confirms that the proposed CV-CNN not only improves overall classification accuracy but also enhances robustness against noise and inter-class similarity. These results highlight the effectiveness of convolutional modeling in complex-valued domains for industrial non-destructive testing applications. To further evaluate the classification performance of the proposed CV-CNN model, a comparative experiment was conducted against a baseline CV-FCNN. Both models were trained and tested on the same dataset under identical conditions to ensure fairness and consistency. The comparison focuses on key evaluation metrics including precision, recall, F1-score, and overall accuracy. Figure 10: Confusion matrix of the CV-CNN model. The comparative results are summarized in Table 2. It is evident that the CV-CNN model outperformed CV-FCNN in most performance indicators. Specifically, CV-CNN achieved an overall classification accuracy of 85.00%, which was approximately 2 percentage points higher than the CV-FCNN's 82.80%. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{4}{c}{**CV-FCNN**} & \multicolumn{4}{c}{**CV-CNN**} \\ \cline{2-10}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Class 0 & 0.7926 & 0.8258 & 0.8089 & 0.8562 & 0.8730 & 0.8645 \\ Class 1 & 0.8021 & 0.7909 & 0.7965 & 0.8357 & 0.8505 & 0.8430 \\ Class 2 & 0.8512 & 0.8571 & 0.8542 & 0.8305 & 0.8140 & 0.8221 \\ Class 3 & 0.8303 & 0.7986 & 0.8142 & 0.8810 & 0.8287 & 0.8541 \\ Class 4 & 0.8561 & 0.8264 & 0.8410 & 0.8442 & 0.8525 & 0.8483 \\ Class 5 & 0.8310 & 0.8397 & 0.8354 & 0.8304 & 0.8188 & 0.8246 \\ Class 6 & 0.8013 & 0.8542 & 0.8269 & 0.8500 & 0.8561 & 0.8530 \\ Class 7 & 0.8321 & 0.8090 & 0.8204 & 0.8767 & 0.8649 & 0.8707 \\ Class 8 & 0.8442 & 0.8118 & 0.8277 & 0.8392 & 0.8362 & 0.8377 \\ Class 9 & 0.8259 & 0.8432 & 0.8345 & 0.8305 & 0.8566 & 0.8434 \\ Class 10 & 0.8432 & 0.8403 & 0.8417 & 0.7803 & 0.8408 & 0.8094 \\ Class 11 & 0.8696 & 0.8362 & 0.8526 & 0.8954 & 0.8589 & 0.8768 \\ Class 12 & 0.8201 & 0.8201 & 0.8201 & 0.8720 & 0.8842 & 0.8780 \\ Class 13 & 0.8305 & 0.8362 & 0.8333 & 0.8562 & 0.8418 & 0.8489 \\ Class 14 & 0.8467 & 0.8438 & 0.8452 & 0.8587 & 0.8556 & 0.8571 \\ Class 15 & 0.8133 & 0.8502 & 0.8313 & 0.8571 & 0.8669 & 0.8620 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification metrics between CV-CNN and CV-FCNN. Figure 11: Confusion matrix of the proposed CV-FCNN model.