## 3 Experimental Results and Analysis
### Analysis of Object Detection Results  
#### 3.5.1 Comparison of Model Experimental Results  
To verify the detection accuracy of YOLOv8-URE in complex scenarios, a large number of workpieces were placed in the original environment, introducing complex stacking relationships and dense occlusions. As shown in Figure 14, for a clear comparison of detection results, 20 workpieces were used as an example. The YOLOv8-URE network was able to detect all the workpieces and classify them correctly, while YOLOv8n missed one target. To further demonstrate the detection accuracy of YOLOv8-URE, the number of workpieces was gradually increased to evaluate its performance under varying conditions. As shown in Table 6, both YOLOv8-URE and YOLOv8n perform well with a small number of targets. However, as the number of targets increases, YOLOv8n shows a clear rise in missed and false detections. In contrast, YOLOv8-URE consistently maintains more accurate detection results, demonstrating better robustness and overall performance in complex, high-density, and occluded scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Algorithm** & **Uni** & **ELA** & \ \begin{tabular}{c} **Improved** \\ **Neck** \\ \end{tabular}  & **ShapeIoU** & **P (\%)** & **R (\%)** & \begin{tabular}{c} **[EMAIL_ADDRESS]** \\ **(\%)** \\ \end{tabular}  & **Parameter** & **GFLOPs** & **FPS** \\ \hline Base & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 96.5 & 90.3 & 96.5 & 3.15 & 8.9 & 614 \\ Base-1 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 94.8 & 92.0 & 96.5 & 2.1  
To evaluate the robustness of the YOLOv8-URE model under conditions resembling real-world industrial environments, this study processed the original scenarios by adding random Gaussian noise and random black screen occlusion, so as to generate blurred scenarios for testing and comparing with the YOLOv8n model. As shown in Figure 15, taking the detection of 20 workpieces as an example, both models experience increased missed and false detections in blurred scenarios. However, YOLOv8-URE consistently achieves a higher detection success rate than YOLOv8n. As shown in Table 7, as the number of targets increases, detection performance in the blurred scene declines more noticeably, especially for YOLOv8n. In contrast, YOLOv8-URE maintains results closer to the actual target count with fewer errors, demonstrating stronger robustness and accuracy under visually degraded conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Missed** & **False** & **Correct** & **Detection** \\
**Targets** & **Detections** & **Detections** & **Detections** & **Accuracy** \\ \hline
15 & 0 \(\pm\) 0/0 \(\pm\) 0 & 0 \(\pm\) 0/0 \(\pm\) 0 & 15 \(\pm\) 0/15 \(\pm\) 0 & 100\% \(\pm\) 0.00\%/100\% \(\pm\) 0.00\% \\
20 & 0 \(\pm\) 0/0.4 \(\pm\) 0.49 & 0 \(\pm\) 0/0.2 \(\pm\) 0.4 & 20 \(\pm\) 0/19.4 \(\pm\) 0.49 & 100\% \(\pm\) 0.00/97\% \(\pm\) 2.50\% \\
25 & 0.2 \(\pm\) 0.4/3.2 \(\pm\) 0.75 & 0.4 \(\pm\) 0.49/0.6 \(\pm\) 0.49 & 24.4 \(\pm\) 0.49/21.2 \(\pm\) 0.75 & 97.6\% \(\pm\) 1.96\%/84.8\% \(\pm\) 2.99\% \\
30 & 2 \(\pm\) 0.63/4 \(\pm\) 0.89 & 0.8 \(\pm\) 0.75/1.8 \(\pm\) 0.4 & 27.2 \(\pm\) 0.75/24.2 \(\pm\) 0.75 & 90.67\% \(\pm\) 2.49/80.67\% \(\pm\) 2.49 \\
35 & 5.4 \(\pm\) 0.49/8 \(\pm\) 0.63 & 2.4 \(\pm\) 0.5/3.2 \(\pm\) 0.75 & 27.2 \(\pm\) 0.8/23.8 \(\pm\) 0.98 & 77.71\% \(\pm\) 2.14/68\% \(\pm\) 2.8 \\
40 & 8 \(\pm\) 1.17/11.8 \(\pm\) 0.75 & 3.8 \(\pm\) 0.75/4.4 \(\pm\) 0.8 & 27.4 \(\pm\) 1.02/23.8 \(\pm\) 0.4 & 68.5\% \(\pm\) 2.25/59.5\% \(\pm\) 1.00 \\ \hline \hline \end{tabular} All values are presented in the format of YOLOv8-URE/YOLOv8n; all results are reported as means \(\pm\) standard deviation over five independent experiments.  
\end{table} 
Table 6: Comparison of original scene detection. Figure 14: YOLOv8-URE and YOLOv8n detection results in the original scene: (**a**) YOLOv8-URE detection results in the original scene; (**b**) YOLOv8n detection results in the original scene.