## 4 Experimental Results
### Test Results and Analysis of Power Cabinet Spring Lock  
In order to verify the effectiveness of the model improvement and study the impact of each improvement measure on the detection effect, a comparative experiment was set up to study the improved network. First, we added MVBlock with the same parameters as GF-YOLOv7 to the backbone network of the YOLOv7-tiny network to obtain the GAE-YOLOv7 network. FF-YOLOv7 was obtained by replacing the neck structure of YOLOv7-tiny with FF-FPN. Then, we used the dataset mentioned in the previous section to train GF-YOLOv7, FF-YOLOv7, GAE-YOLOv7, and the original YOLOv7-tiny network. Table 2 shows the network complexity comparison of the four models. Table 2 shows that after adding MVBlock to the backbone network, the number of layers and parameters of the network model increased, which also led to an increase in the weight file size. After replacing the feature fusion part of the network with FF-FPN, the network weight file size decreased. The weight file of GF-YOLOv7 network after re-parameterization was only 9.1 M, which is conducive to the engineering application of the network. The changes in loss function and [EMAIL_ADDRESS]:0.95 of the four network models were compared and analyzed, and the corresponding change curves are shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Backbone** & \multirow{2}{*}{**Neck**} & **Layer** & **Parameter** & **Model** \\  & **Network** & & & **Quantity** & **Size/mb** \\ \hline YOLOv7-tiny & E-ELAN & FPN+PAN & 200 & 6,025,525 & 12.3 \\ FF-YOLOv7 & E-ELAN & FF-FPN & 184 & 4,166,174 & 8.5 \\ GAE-YOLOv7 & GAE-ELAN & FPN+PAN & 338 & 9,296,693 & 18.9 \\ GF-YOLOv7 & GAE-ELAN & FF-FPN & 322 & 4,929,915 & 9.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithmic model complexity comparison. It is obvious from Figure 8a that the loss function value of the GF-YOLOv7 model decreased faster and the final loss value was the lowest. Figure 8b shows the average accuracy of the four models at the confidence level of 0.5-0.95. Except for the GAE-YOLOv7 network, the loss functions of the other three models increased rapidly at the beginning of training, with small overall fluctuations and good convergence. The average accuracy of GF-YOLOv7 is better than other networks. In conclusion, compared with YOLOv7-tiny, the FF-YOLOv7 model, and the GAE-YOLOv7 model, the GF-YOLOv7 model has better detection performance and recognition effect for substation bounce locks. Table 3 compares the training results of the four networks after 300 rounds of training. The training results show that the GAE-YOLOv7 network with the self-attention mechanism added directly into YOLOv7-tiny has better performance than FF-YOLOv7, although the loss function is smaller than other networks, but the actual performance does not increase or decrease. Analyzing the training process, it is found that GAE-YOLOv7 has poor detection results for very small targets such as floating targets, which affects the overall performance of the network. When the feature fusion network is adjusted to FF-FPN, the effective fusion of information makes the performance of the network slightly better than before. In general, we can see that adding a self-attention mechanism to the network, perceiving global features, and adding the feature fusion network of attention can ensure the detection ability of small targets, and the performance of the target detection network is improved significantly. The experiment used four networks to detect 775 pictures of test data with a total of 901 bounce lock targets. Figure 9 shows the large target bounce lock with a simple background based on YOLOv7-tiny, FF-YOLOv7, GAE-YOLOv7, and GF-YOLOv7 network models. Figure 10 shows the results of mixed bounce lock detection with a complex background based on YOLOv7-tiny, FF-YOLOv7, GAE-YOLOv7, and GF-YOLOv7 network models.