## 3 Results
### Comparative Performance Analysis  
The detection performance and computational efficiency results for actively poured concrete objects during pouring operations are shown in Table 1.  
Table 1 presents the detection performance and computational efficiency for actively poured concrete objects during pouring operations. The evaluation metrics are defined as follows: (1) Accuracy represents the percentage of correctly detected concrete pouring instances out of all test images, calculated as the ratio of true positives to the total number of test images; (2) F1-Score is the harmonic mean of precision and recall, providing a balanced measure of detection performance that accounts for both the model's ability to correctly identify positive cases (recall) and avoid false positives (precision); (3) Misdetection Rate \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Number of Correct Recognized Images**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**F1-Score (\%)**} & \multirow{2}{*}{**Misdetection Rate (\%)**} & \multirow{2}{*}{**Complete Failure Rate (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **Processing Time** \\ **(ms/image)** \\ \end{tabular} } \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline Yolov11 & & & & & \\ Object & 188/232 & 81.03 & 89.52 & 6.03 & 12.93 & 45 \\ Detection & & & & & \\ \hline Ambient Detection & 216/232 & 93.10 & 96.43 & 1.72 & 5.17 & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between YOLOv11 and Ambient Detection. indicates the percentage of cases where non-target objects were incorrectly identified as pouring concrete; (4) Complete Failure Rate represents the percentage of cases where the model failed to detect any concrete pouring activity despite its presence in the image; (5) Processing Time represents the average computational time required to process a single image using an NVIDIA RTX 3090 GPU(NVIDIA Corporation, Santa Clara, CA, USA). In this study, 232 images were selected from a total of 873 field images to serve as the test set for evaluating the object detection performance of the two models. All test images contained at least one instance of a concrete pouring boom, meaning they consisted solely of positive samples. As a result, only true positives (TP) and false negatives (FN) were counted when constructing the confusion matrix, while false positives (FP) and true negatives (TN) were considered to be zero. Accordingly, accuracy and recall yielded identical values, and precision was calculated as 100% due to the absence of false positives. The F1-score, which represents the harmonic mean of precision and recall, was used as the primary metric for comparing the two models. The YOLOv11-based detection model correctly identified 188 out of 232 test images, resulting in an accuracy and recall of 81.03%, and an F1-score of 89.52%. However, it exhibited a relatively high error rate, with 6.03% misdetections and 12.93% complete failures. In contrast, the proposed Ambient Detection model correctly detected 216 images, achieving an accuracy and recall of 93.10%, and an F1-score of 96.43%. The misdetection and complete failure rates were significantly reduced to 1.72% and 5.17%, respectively. Compared to YOLOv11, Ambient Detection showed a 12.07% \(p\) improvement in accuracy and recall, and a 6.91% \(p\) improvement in F1-score. These results suggest that the relationship-based feature enhancement approach in Ambient Detection effectively improved detection reliability. Regarding computational efficiency, the processing time analysis shown in Table 1 reveals that while Ambient Detection requires 65 ms per image compared to 45 ms for YOLOv11, this 44% increase in processing time is justified by the substantial accuracy improvements (12.07% increase in detection accuracy and 71% reduction in false positives). The computational overhead demonstrates that the significant gains in detection reliability outweigh the additional processing cost.