# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 4 Experimental Results and Analysis
### Comparative Experiment  
To demonstrate the effectiveness of the proposed model, we conducted a comparative experiment using the existing small-target data set of transmission lines. We selected Faster-RCNN, RetinaNet, SSD, YOLOv3_spp, YOLOX, and YOLOv5 as the comparison network model. The experimental results are presented in Table 2.  
Table 2 clearly shows that the Mean Average Precision (mAP), which reflects the model's detection performance, exhibits significant improvements with the optimized P2E-YOLOv5 when compared to various other methods. Specifically, compared to Fast RCNN, P2E-YOLOv5 shows a 9.8% improvement in mAP. The improvement over RetinaNet is 18.7%, and over SSD, it is 43.5%. In comparison to the YOLOv3_spp, P2E-YOLOv5 achieves  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mAP\_0.5 (\%)** & **FPS (Frames)** \\ \hline Faster-RCNN & 87.2 & 3.968 \\ RetinaNet & 78.3 & 12.531 \\ SSD & 53.5 & 76.335 \\ YOLOv3\_spp & 79.8 & 49.261 \\ YOLOX & 90.4 & 25.031 \\ YOLOv5 & 93.7 & 101.010 \\ P2E-YOLOv5 (Ours) & 97.0 & 87.719 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental results of different network models. Additionally, mAP_0.5 represents the mean precision at an intersection ratio of 0.5.  
Figure 13: Among them, the horizontal direction is represented by the X-axis and the vertical direction is represented by the \(Y\)-axis. Comparative results of training losses. It is evident that the regression loss, classification loss, and confidence loss of the improved YOLOv5 network converge faster during model training.  
a 17.2% higher mAP. Additionally, compared to YOLOX, P2E-YOLOv5 demonstrates a 6.6% mAP improvement after optimization. Even when compared to the original YOLOv5, P2E-YOLOv5 still shows an overall mAP improvement of 3.3%. These results indicate that the proposed P2E-YOLOv5 detection method in this paper outperforms other detection algorithms significantly in terms of detection accuracy.  
Furthermore, in terms of model detection speed, the optimized P2E-YOLOv5 achieved an impressive 87.719 frames per second, second only to the detection speed of the original YOLOv5. The slight decrease in detection speed compared to the original YOLOv5 is primarily due to the additional computation introduced by the newly added P2 detection head, which slightly increases the model's reasoning time. Nevertheless, when compared to other detection methods, P2E-YOLOv5 maintains a significant advantage, meeting the requirements of real-time detection.  
Additionally, to demonstrate the effectiveness of the proposed method in this paper, we compared it with the latest infrared small object detection method [25] through comparative experiments on the same public data sets, NUDT-SIRST and NUAA-SIRST.  
The experimental results demonstrate that compared to the infrared small object detection method, the proposed method improves the recall rate (R) by 1.2% and 2% on NUST-SIRST and NUAA-SIRST, respectively. In addition, the method achieves an increase of 42.493 frames per second (FPS) on NUAA-SIRST, indicating a faster detection speed.  
In conclusion, the P2E-YOLOv5 algorithm proposed in this paper outperforms other target detection algorithms in all aspects. It effectively addresses the challenge of achieving high detection accuracy for small-target detection in transmission lines and fulfills the need for real-time detection.