## 4 Experiments and Result Analysis
### Comparative Experiments  
To further evaluate the detection performance of the model, a comparative analysis was conducted under identical experimental conditions. The proposed improved model was trained and tested on the constructed dataset and compared with several mainstream object detection models. The detection results of each model during the training process are shown in Table 2.  
From the data in Table 2, it is evident that the improved model in this study outperforms the currently popular mainstream models in terms of the AP values for detecting various types of defects. Overall, in terms of [EMAIL_ADDRESS], the proposed model shows an improvement of 14.89% compared to the Faster-RCNN model, 13.02% compared to the SSD model, 6.1% compared to the RT-DETR model, 19.37% compared to the YOLOv3 model,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**AP/\%**} & \multirow{2}{*}{**[EMAIL_ADDRESS]\%**} \\ \cline{2-2} \cline{4-9}  & **Circular** & **Linear** & **Non-Fusion** & **Non-Penetration** & **Crack** \\ \hline Faster-RCNN & 57.72 & 78.83 & 93.91 & 98.60 & 69.47 & 79.7 \(\pm\) 0.5 & 41.14 & 42.20 \\ SSD & 69.15 & 67.54 & 94.55 & 97.08 & 79.57 & 81.6 \(\pm\) 0.4 & 24.28 & 51.60 \\ RT-DETR & 91.00 & 86.10 & 87.60 & 94.00 & 83.80 & 88.5 \(\pm\) 0.1 & 31.99 & 81.97 \\ YOLOv3 & 83.73 & 68.92 & 79.82 & 85.42 & 58.29 & 75.2 \(\pm\) 0.3 & 12.13 & 62.89 \\ YOLOv5 & 93.10 & 90.80 & 98.00 & 79.90 & 76.70 & 87.7 \(\pm\) 0.2 & 7.02 & 227.27 \\ YOLOv8 & 91.90 & 88.60 & 95.60 & 91.40 & 70.00 & 87.5 \(\pm\) 0.2 & 11.13 & 108.70 \\ YOLOv9 & 92.5 & 91.5 & 91.1 & 82.00 & 78.40 & 87.1 \(\pm\) 0.3 & 7.17 & 107.53 \\ YOLOv10 & 94.20 & 90.20 & 86.10 & 75.60 & 73.20 & 83.9 \(\pm\) 0.3 & **2.70** & **217.39** \\ Ours & **95.70** & **91.30** & **99.50** & **94.20** & **92.00** & **94.6 \(\pm\) 0.2** & 7.02 & 208.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of detection performance across different object detection models.  
Figure 7: Optimal [EMAIL_ADDRESS] values for various types of weld defects detected by original YOLOv5 model and its improved version.  
7.1% compared to the YOLOv8 model, 7.5% compared to the YOLOv9 model, and 10.7% compared to the YOLOv10 model. In addition to detection accuracy, model lightweighting and inference speed are critical considerations in real-world industrial applications. The proposed model contains 7.02 million parameters and achieves an FPS of 208, demonstrating clear advantages in model size and inference speed compared to Faster R-CNN, SSD, RT-DETR, YOLOv3, YOLOv8, and YOLOv9. Although its FPS is 9.06 lower than that of YOLOv10, the proposed model exhibits a notable improvement in detection accuracy. It is evident that under the experimental environment based on an NVIDIA GeForce RTX 3090, the improved model achieves a parameter size of 7.02 M, a detection accuracy of 94.6%, and an inference speed of 208.33 FPS, demonstrating a well-balanced performance across all key metrics. The model maintains a relatively small parameter size while achieving an effective balance between detection accuracy and real-time performance, indicating strong potential for deployment on embedded devices. Therefore, the proposed model can effectively meet the dual requirements of accuracy and real-time performance in steel pipe weld defect detection, making it more suitable for practical inspection scenarios.