# A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset
## 5 Experiments and Results
### Line Field Experiment
#### 5.2.2 Synthetic Dataset  
The main prior series data for synthesizing the dataset are as follows:  
Viewpoint coordinates: the x-axis and y-axis coordinates are O\({}_{\text{o}}\), and the z-axis coordinates are randomly fixed at 2 m to 3 m in the global coordinate system O\({}_{\text{o}}\)-x\({}_{\text{o}}\)y\({}_{\text{o}}\)z\({}_{\text{o}}\).  
Fitting coordinates: the x-axis coordinates change from 1 m to 6 m, the y-axis coordinates change from \(-\)0.5 m to 0.5 m, and the z-axis coordinates change from 2 to 3 m in the global coordinate system O\({}_{\text{o}}\)-x\({}_{\text{o}}\)y\({}_{\text{o}}\)z\({}_{\text{o}}\).  
Fitting types include ceramic tension insulator (XP-70), composite tension insulator (FXBW-10), ceramic post insulator (PS-15), and composite post insulator (FPQ-10).  
Topography type: The topography is all-terrain without restrictions.  
Time data: No restrictions for all seasons and illumination at 0 W/m\({}^{2}\) to 1000 W/m\({}^{2}\).  
The number of generated images for each class of fitting is 1000.  
The important parameters are listed in Table 4:  
Figure 16: Inference results. (**a**) Standard YOLOv5; (**b**) depth-attention YOLOv5.  
Figure 17: Schematic diagram of the line field experiment. (**a**) Overall diagram of the line; (**b**) tension tower; (**c**) tangent tower.  
Figure 18 shows the synthetic dataset for the four classes of fittings. Figure 19 shows the annotated information of the synthetic dataset, including the coordinates of the center point and the size of the annotation box. The fitting positions of the synthetic dataset are mostly concentrated in the upper part of the image, and the fitting sizes are smaller than 0.2 times the image, mostly concentrating in 0.03 times the image size.  
The synthetic dataset has a volume of 4000, containing images and annotation files in VOC format. Before training the model, the synthetic dataset is divided into a training set and a validation set with a ratio of 9:1; the annotation format is converted to YOLO.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Object** & **Fitting Coordinates(m)** & **Fitting Rotation Angle (\({}^{\circ}\))** & **Viewpoint** & **Illumination (W/m\({}^{2}\))** \\ \hline XP-70 & x: [1, 6], \(y\): \([-0.5,0.5]\), \(z\): [2, 3] & \(\alpha\): 0, \(\beta\): \([-30,30]\), \(\gamma\): \([70,110]\), \([-110,-70]\) & [0, 0, 0 2–3] & [0, 1000] \\ FXBW-10 & x: [1, 6], \(y\): \([-0.5,0.5]\), \(z\): [2, 3] & \(\alpha\): 0, \(\beta\): \([-30,30]\), \(\gamma\): \([70,110]\), \([-110,-70]\) & [0, 0, 0 2–3] & [0, 1000] \\ PS-15 & x: [1, 6], \(y\): \([-0.5,0.5]\), \(z\): [2, 3] & \(\alpha\): \([-30,30]\), \(\beta\): \([-30,30]\), \(\gamma\): 0 & [0, 0, 2–3] & [0, 1000] \\ FPQ-10 & x: [1, 6], \(y\): \([-0.5,0.5]\), \(z\): [2, 3] & \(\alpha\): \([-30,30]\), \(\beta\): \([-30,30]\), \(\gamma\): 0 & [0, 0, 2–3] & [0, 1000] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Important parameters for the prior series data.  
Figure 19: The annotated information of the synthetic dataset. (**a**) The coordinates of the center point of the annotation box; (**b**) the size of the annotation box.