# A Visual Fault Detection Algorithm of Substation Equipment Based on Improved YOLOv5
## 3 Experiments and Analysis
### Ablation Experiments  
The performance of YOLOv5 is influenced by various factors in actual application processes, including network architecture, training strategy, and data augmentation. To investigate the impact of these factors on YOLOv5's performance, this paper conducts a series of ablation experiments to systematically analyze different components of the algorithm. The ablation experiments gradually modify the network architecture and training strategy using the control variable method to evaluate their influence on detection performance. Experiment 1: Assess the effectiveness of the proposed variable convolutional CSP module on the original detection algorithm. Experiment 2: Evaluate the effectiveness of replacing PANet with the BiFPN structure in the original detection algorithm. Experiment 3: Examine how adding a detection layer affects performance improvement in the original detection algorithm. After completing these experiments, it is necessary to analyze and explain how different components affect inspection performance. Throughout this experiment's training and testing process, consistent configurations are used for accurate comparison as shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CSP** & **BiFPN** & **Head** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** \\ \hline YOLOv5 & - & - & - & 70.5\% & 65.2\% & 68.5\% \\  & \(\surd\) & - & - & 74.4\% & 69.9\% & 70.1\% \\  & - & \(\surd\) & - & 75.3\% & 70.8\% & 71.5\% \\  & - & - & \(\surd\) & 74.1\% & 68.2\% & 70.6\% \\ pro-YOLOv5 & \(\surd\) & \(\surd\) & \(\surd\) & 76.8\% & 72.9\% & 73.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ablation experiments. In Table 2, row 1 shows the performance of the benchmark YOLOv5s detection algorithm on the dataset in this paper, which shows that the average accuracy is 70.5%, the recall rate is 65.2%, and the [EMAIL_ADDRESS] is 68.5%. Experiment 1: Verify the impact of introducing variable convolutional CSP modules on benchmark detection in backbone networks. This experiment uses two different networks: one using standard convolutional layers and one using variable convolutional layers. In the second row of Table 2, the experimental results of the introduction of the variable convolution module are shown, and it is found that the introduction of the variable convolution CSP module improves the extraction ability of the network to target features and the performance improvement is significantly compared with the original algorithm, with the [EMAIL_ADDRESS] reaching 70.1%, an increase of 1.6%. Therefore, the variable convolutional CSP module proposed in this paper has better performance than the original detection algorithm, and the performance can be further improved by adding more variable convolutional layers to the backbone network. However, when there are too many variable convolutional layers, performance begins to degrade. Through the analysis, it was also found that the use of variable convolution can increase the depth and width of the network with a small amount of computation. In addition, the performance differences between two networks using variable convolution and standard convolution are compared, and the results show that under the same conditions, networks using variable convolution are superior to networks using standard convolution. Experiment 2: Verify the influence of replacing PANet with BiFPN structure on improving model performance. The third row in Table 2 shows the experimental results of replacing the original PANet with the BiFPN structure, which improves the model recall to 70.8%, the accuracy to 75.3%, and the [EMAIL_ADDRESS] also improves to a small extent. Experimental results show that the use of BiFPN in YOLOv5 can significantly improve the performance of small target detection. It is found that when using a large number of small target datasets, the network using BiFPN can improve detection performance.