# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 4 Experimental Results and Analysis
### Experiment  
In this experiment, the small-target data set of transmission lines was subjected to data processing, and the analysis is presented in Figure 7.  
In Figure 7, the darker the blue area, the greater the concentration of small targets. The left figure displays the distribution of the center coordinates for small target objects in the transmission line's small-target data set. The coordinates are relative coordinates, which are relative to the coordinates of the whole transmission line small target image, and its size range is mapped to (0, 1). The central coordinates of the small target are observed to be mainly concentrated in the range (0.4-0.6, 0.0-1.0). The right figure displays the height and width distribution of small target objects relative to the whole image. It is evident that the small target objects are significantly smaller compared to the entire image. In fact, a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**P** & **R** & **AP** & **mAP** \\ \hline \(P=\frac{\mathbf{N}_{\mathit{F}}}{\mathbf{N}_{\mathit{Tr}}+\mathbf{N}_{ \mathit{Tr}}}\) & \(R=\frac{\mathbf{N}_{\mathit{F}}}{\mathbf{N}_{\mathit{Tr}}+\mathbf{N}_{ \mathit{FN}}}\) & \(AP=\int_{0}^{1}P(R)dR\) & \(mAP=(\sum\limits_{t}^{N}Api)/N\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: In this Table, \(\mathrm{N}_{\mathit{TP}}\), \(\mathrm{N}_{\mathit{FP}}\), and \(\mathrm{N}_{\mathit{FN}}\) represent the number of samples predicted correctly, the number of samples predicted incorrectly, and the number of samples not recognized, and N denotes the total number of categories specified by the model.  
majority of small target objects account for less than 0.12% of the whole image area (the definition of relative size for small targets is elaborated in the introduction Section 1). This indicates that most of the targets in this data set are indeed small targets.  
Subsequently, training experiments were conducted on the small-target data set of transmission lines, and the original YOLOv5 training results were compared with the improved P2E-YOLOv5 training results:  
The test results before the algorithm improvement are shown in Figure 8:  
Figure 8: The test results prior to enhancement depict (**a,b**) as a pair of randomly selected images for the purpose of evaluation.  
Figure 7: Among them, the horizontal direction is represented by the \(X\)-axis and the vertical direction is represented by the \(Y\)-axis. Data analysis of transmission line small-target data set.  
Figure 11: Among them, the horizontal direction is the \(X\)-axis and the vertical direction is the \(Y\)-axis. Comparison results of recall rates.  
Figure 12: Among them, the horizontal direction is the \(X\)-axis and the vertical direction is the \(Y\)-axis. Average precision mAP comparison results.  
From the above comparison results, it is evident that the improved P2E-YOLOv5 outperforms the original YOLOv5 in terms of accuracy, recall rate, average accuracy, and loss index. The improved model shows a significant enhancement in detecting objects with low resolution and small sizes in images.