# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 4 Experimental Results and Analysis
### Influence Factors and Result Analysis
#### 4.2.3 Detection Results Using Different Detection Methods  
In order to verify the effectiveness of the improved YOLOv4 lightweight detection model proposed in this paper, four mainstream object detection models were constructed for comparison: Faster RCNN, SSD, YOLOv3, and YOLOv4. Then, the same training set consisting of the irregular behavior dataset of substation personnel was used to train these models, which were then used to detect images in the same test set. The detection results are shown in Table 4.  
From the results in Table 4, it can be seen that the series of YOLO detection algorithms are able to accurately and quickly detect irregular behaviors of substation personnel by  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**AP of Each Class (\%)**} & \multirow{2}{*}{**mAP (\%)**} & \multirow{2}{*}{**FPS (Pictures/s)**} \\ \cline{2-2} \cline{5-8}  & **Badge** & & & & & & & \\ \hline Faster RCNN & 34.76 & 46.40 & 80.97 & 61.63 & 85.01 & 36.15 & 57.49 & 12.98 \\ SSD & 47.76 & 60.10 & 84.72 & 60.19 & 90.96 & 54.84 & 66.43 & 28.30 \\ YOLOv3 & 80.23 & 69.46 & 93.78 & 63.29 & 93.88 & 58.16 & 76.47 & 29.48 \\ YOLOv4 & 80.96 & 81.86 & 94.58 & 66.47 & 93.84 & 59.73 & 79.57 & 22.08 \\ Improved YOLOv4 & 84.88 & 75.96 & 94.30 & 74.96 & 92.80 & 65.76 & 81.44 & 38.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection results using different detection methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ratios**} & \multicolumn{6}{c}{**AP of Each Class (\%)**} & \multirow{2}{*}{**mAP (\%)**} \\ \cline{2-2} \cline{5-8}  & **Badge** & & & & & & \\ \hline
5:5 & 82.15 & 74.51 & 92.97 & 62.27 & 92.02 & 59.94 & 77.31 \\
6:4 & 83.53 & 76.40 & 94.40 & 63.63 & 92.41 & 63.03 & 78.90 \\
7:3 & 84.01 & 74.80 & 94.93 & 65.84 & 91.95 & 60.31 & 78.64 \\
8:2 & 83.92 & 76.88 & 95.51 & 68.56 & 93.15 & 70.07 & 81.35 \\
9:1 & 84.88 & 75.96 & 94.30 & 74.96 & 92.80 & 65.76 & 81.44 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection results under different detection methods.  
virtue of their structural advantages. The mAP was over 76%, which is higher than Faster RCNN and SSD. Moreover, in the series of YOLO algorithms, the mAP of YOLOv4 can reach 79.57, which is 3.1% higher than that of YOLOv3, although the detection speed (FPS) is reduced on account of the complex structure of YOLOv4. However, the improved YOLOv4 lightweight detection model proposed in this paper can increase the mAP to 81.44% while increasing the FPS to 38.06 pictures/s; that is, the time required to detect an image is about 26.27 ms, which is almost 1.72 times the FPS of YOLOv4. From the perspective of the AP of each class, although the detection accuracy of YOLOv4 for the person, helmet, and glove categories is slightly higher than that of the improved YOLOv4, the improved YOLOv4 can achieve more accurate detection on difficult-to-detect target categories, such as badge, operatingbar, and powerchecker. From the results in Table 5, it can be seen that the improved YOLOv4 is generally better than Faster RCNN, SSD, YOLOv3, and YOLOv4. The F1-score of the improved YOLOv4 model in the glove category is only 0.08 lower than YOLOv4, and the F1-score of improved YOLOv4 in the person category is only 0.02 lower than YOLOv3.