## 4 Discussion  
### Comprehensive Analysis of Object Recognition Performance  
The Ambient Detection model demonstrated superior performance compared to YOLOv11 in terms of accuracy, F1 score, misdetection rate, and complete failure rate. Experimental results showed that Ambient Detection achieved a 93.10% detection success rate versus YOLOv11's 81.03%, representing a 12.07% performance improvement. While  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{Configuration} & **Detection** & \multirow{2}{*}{**Success Rate (\%)**} & \multirow{2}{*}{**Total Failures**} & \multirow{2}{*}{**Misdetections**} & \multirow{2}{*}{**Complete Failure**} \\  & **Success** & & & & \\ \hline \(D_{norm}+A_{norm}+S_{R}\) & 216/232 & 93.10 & 16 & 4 & 12 \\ \(D_{norm}+A_{norm}\) & 206/232 & 88.79 & 26 & 6 & 20 \\ \(D_{norm}+S_{R}\) & 203/232 & 87.50 & 29 & 7 & 22 \\ \(A_{norm}+S_{R}\) & 187/232 & 80.60 & 45 & 11 & 34 \\ \(D_{norm}\) only & 190/232 & 81.90 & 42 & 10 & 32 \\ \(A_{norm}\) only & 175/232 & 75.43 & 57 & 14 & 43 \\ \(S_{R}\) only & 178/232 & 76.72 & 54 & 14 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study: impact of each relationship factor on detection performance (232 test images).  
YOLOv11 maintained reasonable accuracy under ideal conditions, its performance deteriorated significantly in scenes with partial occlusions or visual confusion. In contrast, Ambient Detection maintained stable performance in complex environments by leveraging inter-object relationships.  
The Ablation Study results clearly illustrate the impact of each component on the model's performance. The complete model utilizing all three relationship factors (distance, attribute, and spatial) recorded the highest success rate at 93.10%, while performance decreased substantially when using single factors alone. Notably, the distance relationship (\(\mathbf{D_{norm}}\)) proved to be the most influential factor, achieving an 81.90% success rate independently. In comparison, attribute (\(\mathbf{A_{norm}}\)) and spatial (\(\mathbf{S_{R}}\)) relationships achieved only 75.43% and 76.72% success rates, respectively, when used independently.  
These findings indicate that the physical proximity between pouring equipment and concrete serves as the most critical signal in detecting pouring operations. Configurations excluding the distance relationship (\(\mathbf{A_{norm}}+\mathbf{S_{R}}\)) showed significant performance degradation to 80.60%. In contrast, excluding spatial (\(\mathbf{D_{norm}}+\mathbf{A_{norm}}\)) or attribute (\(\mathbf{D_{norm}}+\mathbf{S_{norm}}\)) relationships resulted in relatively smaller decreases to 88.79% and 87.50%, respectively.  
While attribute and spatial relationships showed limited performance individually, they complementarily enhanced detection performance when combined with the distance factor. Attribute relationships contributed to distinguishing between visually similar but functionally different objects, while spatial relationships provided contextual information based on object positioning. Failure type analysis revealed that YOLOv11's complete failure rate of 12.93% and misdetection rate of 6.03% were significantly reduced to 5.17% and 1.72%, respectively, with Ambient Detection. This demonstrates the relationship-based approach's robustness in complex construction environments.