## 3 Analysis of Experimental Results
### Analysis of Information Recognition Effectiveness for Secondary Cabinet Terminal Blocks
#### 3.5.2 Analysis of Physical Entities Recognition Results for Secondary Screen Terminal Blocks  
In Section 2.2, we introduced a model specifically designed for recognizing the physical entities of terminal blocks. To evaluate the target detection performance of the YOLOv7-DBAH model, we conduct comparative experiments using the same test data. The results, which encompass a comprehensive comparison of category indices, are presented in Figure 10 and Table 5.  
The experimental outcomes demonstrate that the YOLOv7-DBAH model excels in average index results across three types of targets, with its lowest indicator surpassing 0.91. This performance places it distinctly ahead of the YOLOv7-DBH and YOLACT models, which form the second tier, with index values around 0.85. The conventional U-Net segmentation network lags slightly behind, maintaining an evaluation index near 0.80. These findings indicate that the YOLOv7-DBH structure, as developed in this study, outperforms baseline segmentation networks in detecting and identifying terminal strip-related physical components. Moreover, the YOLOv7-DBAH model, which integrates spatial and channel attention mechanisms, enhances the intermediate feature map with a higher proportion of target recognition-related information. This enhancement enables different detection heads to learn features pertinent to the target, culminating in superior physical component recognition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **mAP** & **Recall** & **False Negative Rate** \\ \hline SSD & 0.492 & 0.482 & 0.473 & 0.527 \\ R-CNN & 0.598 & 0.554 & 0.549 & 0.451 \\ YOLOv5 & 0.719 & 0.629 & 0.685 & 0.315 \\ YOLOv7 & 0.791 & 0.653 & 0.712 & 0.288 \\ Our & **0.887** & **0.821** & **0.889** & **0.111** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the proposed model on different entities in schematic diagrams. Figure 10: Comparison of average results of different models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** \\ \hline U-Net & 0.7930 & 0.8037 & 0.7983 \\ YOLACT & 0.8398 & 0.8571 & 0.8483 \\ YOLOv7-DBH & 0.8768 & 0.8735 & 0.8743 \\ Our & **0.9257** & **0.9173** & **0.9208** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of average results of different models. Analyzing the sub-index results for the three component types, including terminal numbers, reveals that the ratio of positive to negative pixel areas in the sample significantly influences the precision and recall calculations. Even with the same recognition model, the terminal number and number tube, benefiting from a larger target count, demonstrate a marked improvement in accuracy compared with the terminal inscription target. However, the recall index results show that the DB-type detection head brings the terminal inscription target's performance closer to that of the other two target types. This suggests that the DBAH detection head, designed with an attention mechanism, tends to enhance the feature extraction of target types in imbalanced samples. Despite this, the YOLOv7-DBH model does not outperform in recognizing terminal inscription targets in the F1 score comparison. However, its mean results slightly exceed those of the YOLACT model, primarily due to better performance with terminal number and number tube targets. The YOLOv7-DBAH model, augmented with the attention mechanism, shows improvements across all indicators for the three types of physical component recognition. It not only raises the precision and recall rates, which were already higher than the YOLACT model, to above 0.91 but also enhances the F1 score for terminal inscription targets from 0.83 to over 0.89. This significant improvement reaffirms the efficacy of the DBAH detection head in enhancing target recognition. An example of the recognition results is shown in Figure 11, based on an actual photograph of a terminal block. The original photo in the figure is significantly affected by the background.