## 4 Results and Discussion  
Regarding the current improvements, we conducted ablation experiments on various modules, as shown in Table 3. YOLOv5-p1 replaces the backbone with ConvNextv2, YOLOv5-p2 adds SCConv between blocks based on p1, YOLOv5-p3 introduces the SE attention mechanism to all modules based on p2, and Ours adds SCConv between blocks based on p3. Finally, our algorithm improves the neck and adds the BiFormer structure based on YOLOv5-p3. From the results, the performance of the network has gradually improved during the enhancements. SCConv significantly enhances the accuracy of the modules, as its ability to filter spatial redundant information contributes to the effective enhancement of the model, while the attention module primarily improves the intersection over union of images. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Describe** & **Value** \\ \hline fl\_gamma & focal loss gamma & 1.5 \\ hsv\_h & HSV-Hue augmentation & 0.015 \\ hsv\_s & HSV-Saturation augmentation & 0.7 \\ hsv\_v & HSV-Value augmentation & 0.4 \\ degrees & image rotation & 0.0 \\ translate & image translation & 0.1 \\ scale & image scale & 0.5 \\ shear & image shear & 0.01 \\ perspective & image perspective & 0.0 \\ flipud & image flip up-down & 0.5 \\ fliplr & image flip left-right & 0.5 \\ mosaic & image mosaic & 0.0 \\ mixup & image mixup & 0.0 \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters and values in the dataset augmentation. We set the confidence threshold to 0.25 and the IoU overlap to 0.45 to draw the confusion matrix for the validation set using the model, as shown in Figure 7. From the confusion matrix, it can be seen that this algorithm effectively distinguishes between the background and targets in target recognition. In terms of quantity, out of 223 images, 177 were correctly identified, with only 46 failing to accurately identify the position of the ureteral opening. Background misidentification as the ureteral opening occurred only 1 time, meaning that while it slightly increases the difficulty for doctors to discern, it greatly enhances the indication of the ureteral opening position. Such results effectively help doctors save time and effort by reducing trial and error caused by misjudgments, thereby improving the quality of surgical procedures and reducing doctors' energy consumption. Based on the confusion matrix, we discuss the current state of related research. Due to the incomplete disclosure of the implementation code and corresponding datasets in [42], we can only make a preliminary comparison by rigidly comparing the experimental results on our respective datasets. Based on the provided confusion matrix data, we can easily derive the corresponding P (precision) and R (recall) from Equations (18) and (19). The performance of their baseline SSD model is 0.860 and 0.794, with improved performance of 0.884 and 0.926. Compared to our model, the higher recall rate and lower accuracy would clearly increase the response burden on doctors. This is one of our primary goals in model improvement: to enhance accuracy and reduce the direct workload of doctors' decision-making. Since previous work was based on tracking algorithms to achieve real-time detection, we cannot directly evaluate the execution efficiency of different algorithms on different platforms. However, in Table 3, we compare the computational cost and detection speed of our algorithm with the traditional real-time target detection YOLO algorithm and observe no significant disadvantages. This provides an interesting comparison point with previous work. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **GFLOPs** & **Inference Time** & \(p\)**-Value** & **R-Value** & **[EMAIL_ADDRESS]** \\ \hline YOLOv8 & 78.7 & 3.5 ms & 0.830 & 0.771 & 0.850 \\ YOLOv7 & 105.1 & 4.6 ms & 0.885 & 0.762 & 0.815 \\ YOLOv5 & 47.9 & 3.7 ms & 0.894 & 0.758 & 0.828 \\ Ours & 59.8 & 5.7 ms & 0.928 & 0.756 & 0.896 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Network structure improvement comparison.