## 4 Discussion
### Performance Analysis
#### 4.2.2 Comparative Experiment of DSEF-DenseNet-DT  
The recognition results of color texture features for DT classification are displayed in Table 5. The recognition results of the size features used for DT classification are shown in Table 6. The four performance indicators of the CTD-based DT (CTD-DT) method were 75.3%, 70.7%, 72.45% and 0.716, respectively. The four performance indicators of the SD-based DT (SD-DT) method were 80%, 80.8%, 77.25% and 0.789, respectively. The average recognition accuracy, precision and recall of CTD-DT and SD-DT methods are below 81%, the F1 score is below 0.8, and the recall of C2 and C3 classes is below that of C4, which implies that classes labeled C2 and C3 are easily misclassified into other classes. The findings above indicate that the method of extracting features by digital image processing technology cannot capture some of the detailed information required by the classifier, and the extracted features contain more redundancy and noisy information. The experimental outcomes of DenseNet-DT are displayed in Table 7 when DT is connected to various completely connected layers. It can be found that when DT is connected to fully connected layer fc2, the recognition accuracy, precision and recall are below 65%, and the F1 score is even below 0.5. Therefore, the working condition recognition method when DT is connected to fc2 is infeasible. DenseNet-DT offers the highest metrics when DT is connected to fully connected layer fc1, with an accuracy and recall of more than 80% and an F1 score of more than 0.8. As a result, DT is directly connected to DenseNet's fully connected layer fc1 in the proposed DSEF-DenseNet-DT method, the structure of which is shown in Figure 10b. As illustrated in Figure 14, DenseNet-DT is compared with CTD-DT and SD-DT methods in order to confirm that the deep learning network has a strong detail feature extraction capacity. Compared with the CTD-DT and SD-DT methods, the accuracy and average accuracy of C2 and C3 with the DenseNet-DT method were increased by at least 15%, 5% and 6%, respectively. Except for the precision of the DenseNet-DT method, which \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**Category**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1 score**} \\  & & **C1** & & & & & & \\ \hline C1 & 6 & 4 & 1 & 0 & 1 & 66.7\% & 80\% & 66.7\% & 0.727 \\ C2 & 60 & 1 & 44 & 4 & 11 & 73\% & 86.3\% & 73\% & 0.791 \\ C3 & 38 & 0 & 6 & 27 & 5 & 71\% & 84.4\% & 71\% & 0.771 \\ C4 & 46 & 0 & 0 & 1 & 45 & 98\% & 72.6\% & 98\% & 0.834 \\ Total & 150 & 5 & 51 & 32 & 62 & 80\% & 80.8\% & 77.25\% & 0.789 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of SD-DT using size features for DT classification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fully Connected Layer** & **Accuracy** & **Precision** & **Recall** & **F1 score** \\ \hline fc0 & 82.7\% & 80.7\% & 75.4\% & 0.78 \\ fc1 & 86\% & 79.6\% & 80.7\% & 0.801 \\ fc2 & 64\% & 47.5\% & 49.4\% & 0.484 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results of DenseNet-DT when DT is connected to different fully connected layers. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**Category**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1 score**} \\  & & **C1** & & & & & & \\ \hline C1 & 6 & 4 & 0 & 0 & 2 & 66.7\% & 57.1\% & 66.7\% & 0.615 \\ C2 & 60 & 0 & 48 & 10 & 2 & 80\% & 77.4\% & 80\% & 0.787 \\ C3 & 38 & 1 & 11 & 23 & 3 & 60.5\% & 63.9\% & 60.5\% & 0.622 \\ C4 & 46 & 2 & 3 & 3 & 38 & 82.6\% & 84.4\% & 82.6\% & 0.835 \\ Total & 150 & 7 & 62 & 36 & 45 & 75.3\% & 70.7\% & 72.45\% & 0.716 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of CTD-DT using color texture features for DT classification.