# Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
## 3 Results and Discussions
### Classification Performance Evaluation  
According to whether the classification results are correct, TP, TN, FP, and FN can be determined. TP means that the classification result is true and positive, TN means true negative, FP means false positive, and FN means false negative.  
Recall, precision, accuracy, specificity and F1-score were employed as classification performance indicators to evaluate different methods. They are defined as follows [55].  
\[\text{Recall}=\frac{TP}{TP+FN}. \tag{8}\]  
\[\text{Precision}=\frac{TP}{TP+FP}. \tag{9}\]  
\[\text{Specificity}=\frac{TN}{FP+TN}. \tag{10}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}. \tag{11}\]  
\[\text{F1-score}=\frac{2 TP}{2 TP+FP+FN}. \tag{12}\]  
Recall measures the proportion of actual positives that are correctly identified as such. Specificity represents the proportion of actual negatives that are correctly identified as such. Accuracy is defined as the proportion of all samples that have been successfully classified. Precision is the ratio of samples correctly classified as positive to all the samples that are classified. F1-score is the harmonic mean of precision and sensitivity. When the above performance index is greater, the classification performance is better.  
The above-mentioned five indicators of different blister classification methods are listed in Table 3. The method proposed in this paper had the greatest value for all performance indicators, meaning that the proposed CNN based method was superior to other classification methods for blister recognition of PLB sheets. The performance comparison is also shown in Figure 7, which indicates that the proposed method had the best classification performance. The improved CNN architecture with optimization based training method was efficient for blister detection when trainable weight parameters were added to skip connections in dense block.  
To observe the effectiveness and efficiency of the proposed FPA optimization in this paper, an ablation study was performed on the proposed FPA optimization. The FPA optimization based method was compared with the method without FPA optimization in the experiment. The results of the comparison are shown in Table 4, and the data are also illustrated in Figure 8.  
Figure 8: Classification performance comparison on FPA optimization.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **NN** & **SVM** & **STM** & **DenseNet** & **ResNet** & **VGG** & **RCNN** & **Proposed** \\ \hline Recall & 0.680 & 0.824 & 0.932 & 0.959 & 0.954 & 0.937 & 0.937 & 0.982 \\ Precision & 0.799 & 0.855 & 0.898 & 0.949 & 0.934 & 0.923 & 0.939 & 0.993 \\ Accuracy & 0.724 & 0.813 & 0.902 & 0.948 & 0.936 & 0.920 & 0.931 & 0.986 \\ Specificity & 0.781 & 0.798 & 0.864 & 0.934 & 0.914 & 0.899 & 0.922 & 0.991 \\ F1-score & 0.735 & 0.839 & 0.915 & 0.954 & 0.944 & 0.930 & 0.938 & 0.988 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification performance of different methods for blister detection.  
Figure 7: Classification performance comparison of different methods.  
By observing the data in Table 4 and Figure 8, it can be seen that the performance was obviously improved when applying the proposed FPA optimization. Because FPA optimization could find the optimum learning rate, the training process was more efficient and the classification results were improved.