## 4 Experiments
### Ablation Experiment
#### 4.3.1 Replacing the C3k2 Module with the Ghost Conv Module  
As shown in Table 5, after incorporating the Ghost Conv module, the model improved by 1.56% in the mAP@80 metric but decreased by 1.24% in the stricter mAP@75-95 metric. Additionally, as shown in Table 6, the model achieved a speed detection accuracy of 89.65% in 60-frame videos, the best performance across all the experimental settings, although its speed measurement results fluctuated significantly across different scenarios, indicating poor stability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**YOLOv11**} & \multirow{2}{*}{**Ghost Conv**} & \multirow{2}{*}{**Concat**} & \begin{tabular}{c} **Variance** \\ **(30 Frames)** \\ \end{tabular} & \begin{tabular}{c} **Variance** \\ **(60 Frames)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **((m/s)2)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **((m/s)2)** \\ \end{tabular} & \
\begin{tabular}{c} **(60 Frames)/(\%)** \\ \end{tabular} &
\begin{tabular}{c} **(60 Frames)/(\%)** \\ \end{tabular} \\ \hline \(\check{\vee}\) & & & 0.0177 & 0.0411 & 94.44 & 88.97 \\ \(\check{\vee}\) & \(\check{\vee}\) & & 0.0184 & 0.0427 & 94.51 & **89.65** \\ \(\check{\vee}\) & & \(\check{\vee}\) & 0.0148 & 0.0393 & 94.44 & 89.41 \\ \(\check{\vee}\) & \(\check{\vee}\) & \(\check{\vee}\) & **0.0122** & **0.0392** & **94.83** & 89.40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Target-tracking ablation experiments (\"\(\check{\vee}\)\" indicates that the corresponding module is enabled).  
Figure 8: Real-world tracking and speed estimation under complex backgrounds using handheld drilling on a wall. (**Top**: original image; **bottom**: enlarged view).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**YOLOv11**} & \multirow{2}{*}{**Ghost Conv**} & \multirow{2}{*}{**Concat**} & \multirow{2}{*}{**mAP@80/(\%)**} & \multirow{2}{*}{**mAP@75-95/(\%)**} & \multirow{2}{*}{**fps**} & \multirow{2}{*}{**Params/M**} \\ \hline \(\check{\vee}\) & & & 71.36 & 40.94 & 111 & 24.20 \\ \(\check{\vee}\) & \(\check{\vee}\) & & 71.75 & 39.70 & **114** & **22.24** \\ \(\check{\vee}\) & & \(\check{\vee}\) & 74.18 & **42.25** & 75 & 45.39 \\ \(\check{\vee}\) & \(\check{\vee}\) & \(\check{\vee}\) & **76.04** & 41.73 & 76 & 43.51 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Target detection ablation experiments (“\(\check{\vee}\)\" indicates that the corresponding module is enabled).  
This indicates that, while the Ghost Conv module effectively reduces model complexity and improves inference speed (fps), it still has limitations in feature representation. Ghost Conv generates redundant features by applying linear transformations to a small number of primary features. Although this approach maintains high accuracy in static or low-complexity scenes, it struggles to capture key visual cues--such as object boundaries, shape deformations, and motion blur--in scenarios involving fast motion or complex appearances. As a result, despite its strong performance on the mAP@80 metric, its accuracy drops under the stricter mAP@75-95 evaluation due to insufficient modeling of subtle displacements and fine structural details. In summary, while Ghost Conv provides computational benefits, it also challenges the robustness of object detection and requires complementary modules to enhance feature expressiveness.