## 3 Results
### Qualitative Analysis and Statistical Significance Analysis  
To provide a more comprehensive evaluation of the proposed approach, this section presents qualitative examples alongside statistical validation of the performance differences. Figure 4 illustrates representative examples that demonstrate the superior performance of Ambient Detection in challenging scenarios. These qualitative results support the quantitative findings presented in Table 1, highlighting how the relationship-based approach effectively addresses the key limitations of conventional object detection methods in construction environments.  
Figure 4 demonstrates two critical challenges overcome by the proposed approach: complete failure due to boundary ambiguity and misdetection caused by visual similarity between objects. In the complete failure case, Figure 4a shows YOLOv11 failing to detect actively poured concrete due to visual similarity with already placed surface concrete. Figure 4b demonstrates how Ambient Detection successfully identifies the target concrete through spatial and distance relationship analysis, overcoming the boundary ambiguity challenge. In the misdetection scenario, Figure 4c illustrates YOLOv11 incorrectly detecting multiple non-target objects as pouring concrete due to visual similarity. Figure 4d shows how Ambient Detection accurately detects only the actual pouring concrete by leveraging relationship factors to eliminate misdetections.  
To further validate these qualitative observations and determine whether the observed performance difference between the two models is statistically significant, [PERSON]'s test was applied. This non-parametric test is particularly suitable for paired nominal data and is commonly used to compare the performance of two models on the same test dataset.  
Table 2 presents the contingency table used for [PERSON]'s test, categorizing all 232 test images based on the success or failure of detection by each model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Description** & **Number of Images** \\ \hline A & Both YOLOv11 and Ambient Detection succeeded & 180 \\ B & YOLOv11 succeeded, Ambient Detection failed & 8 \\ C & Ambient Detection succeeded, YOLOv11 failed & 36 \\ D & Both YOLOv11 and Ambient Detection failed & 8 \\ Total & 232 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contingency table for [PERSON]’s test comparing YOLOv11 and Ambient Detection.  
Figure 4: Concrete pouring detection comparison: (**a**) YOLOv11 complete failure due to boundary ambiguity; (**b**) Ambient Detection success overcoming boundary ambiguity; (**c**) YOLOv11 misdetection of non-target objects; and (**d**) Ambient Detection accurate detection eliminating misdetections.  
[PERSON]'s test focuses on the discordant pairs--cases where one model succeeded while the other failed. From Table 2, these are categories B (YOLOv11 succeeded, Ambient Detection failed) and C (Ambient Detection succeeded, YOLOv11 failed).  
The calculated \(\chi^{2}\) value of 16.57 exceeds the critical value of 3.84 for a significance level of 0.05 with 1 degree of freedom. This result indicates that the performance difference between the YOLOv11 and Ambient Detection models is statistically significant (\(p<0.05\)).