# Enhanced Detection of Subway Insulator Defects Based on Improved YOLOv5
## 4 Experimental Results and Analysis
### Field Detection and Visualization Analysis  
This study further evaluates the enhanced algorithm's practical application by deploying the refined model in real-world conditions, analyzing 200 field images. China's subway system includes the extremely strict management of subway operation and maintenance, as the corresponding subway staff can only enter the operation in the early hours of the subway operation window period. Once the staff find a defective insulator, it will be replaced with a normal insulator. The contact network dataset used in this study was obtained on certain days, and we did not obtain a real image of the defective insulators in the line. Therefore, we only validate insulator recognition in real scenarios in this part. The results, detailed in Table 6, solely encompass insulator detections due to the absence of defects in the real-world images. Figure 10 visually illustrates the field detection, with insulators delineated by red boxes. This real-world application exhibits the algorithm's efficacy and utility in practical settings.  
The visual results presented in Figure 10 and the data in Table 6 indicate that the YOLOv5-GhostNet-ECA model proposed in this study enhances the detection velocity relative to the baseline model, while also improving the identification efficacy for smaller insulators, a direct benefit of integrating the ECA-Net attention mechanism, as evidenced by a marked rise in confidence levels. Notably, the model maintains robust detection and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Environment \({}^{*}\)**} & \multirow{2}{*}{**mAP (\%)**} & **Insulator** & **Defect AP** & \multirow{2}{*}{**Size (MB)**} & \multirow{2}{*}{**FPS**} \\  & & **AP (\%)** & & **(\%)** & \\ \hline Experimental setting (v5) & 95.46 & 96.83 & 94.09 & 15 & 30 \\ Real-life scenarios (v5) & 95.98 & 95.98 & 0 & 15 & 30 \\ Experimental setting (our) & 96.57 & 97.06 & 96.08 & 9 & 90 \\ Real-life scenarios (our) & 98.43 & 98.43 & 0 & 9 & 90 \\ \hline \hline \end{tabular}  
* Note: “our” refers to the algorithm proposed in this study and “v5” represents the use of the original YOLOv5 model.  
\end{table}
Table 6: Comparative detection results under different environments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **mAP** & \multirow{2}{*}{**mAP @0.5 (\%)**} & **Insulator** & **Defect** & \multirow{2}{*}{**Size (MB)**} & \multirow{2}{*}{**FPS**} \\  & **@0.50.95 (\%)** & & **[EMAIL_ADDRESS] (\%)** & & & \\ \hline YOLOv3 & 76.48 & 88.94 & 92.61 & 86.93 & 235 & 25 \\ YOLOv4 & 79.17 & 89.50 & 92.35 & 87.95 & 243 & 22 \\ YOLOv5 & 84.62 & 95.06 & 96.83 & 94.09 & 15 & 30 \\ YOLOv8 & 85.08 & 95.11 & 96.87 & 94.15 & 24 & 27 \\ SSD & 78.83 & 89.07 & 94.23 & 86.25 & 88 & 59 \\ Faster R-CNN & 83.97 & 94.68 & 94.73 & 94.65 & 519 & 19 \\ YOLOv5-GhostNet-ECA & 86.58 & 96.43 & 97.06 & 96.08 & 9 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of results from different algorithms.  
recognition capabilities under diverse environmental conditions, including fog. These findings demonstrate the model's advanced performance in detecting and categorizing targets across varying environmental contexts, affirming its superiority, especially in visibility-compromised situations.