## 4 Result Analysis of Detection Model
### Testing Phase  
The confusion matrix is a commonly employed tool for evaluating the performance of a model [40]. The evaluation assesses the performance of individual classes and presents various performance metrics of the model, including precision, recall, specificity, F1 score and others. This analysis examines the distribution of accurate and inaccurate predictions obtained from the model, aiding in the assessment of the model's reliability. Figure 9 depicts the confusion matrix generated for our suggested model. The matrix is additionally used to estimate several performance metrics, as indicated in Tables 3 and 4. The F1 confidence curve provides insights about the model's predictions and helps to balance between the precision and recall [41]. Figure 10 depicts the F1 score of the model in relation to different confidence threshold values, as well as the confidence scores affiliated with its predictions. In our study, the F1 score is tuned at 89% to balance both precisions and recall. Moreover, this value was determined at a confidence threshold of 0.524, which was the optimal threshold. This observation suggests that the model has satisfactory performance in terms of precision and recall across all classes at the aforementioned value. The precision-recall curve is typically a curve that starts at a high value of precision and gradually decreases as recall increases [42]. It provides a trade-off between precision and recall for different classification thresholds. According to Figure 10, the model attained a  
Figure 8: Box loss, Class Loss, DFL loss, map during validation phase during model training. Figure 9: Confusion matrix of the detection model. precision value of 98.1% for the volume class, which is the highest value obtained. The total precision value across all classes is reported to be 92.5%, with a threshold set at 0.5. Precision confidence evaluates the accuracy of accurate positive predictions, highlighting the model's reliability [43]. On the other hand, recall confidence refers to the level of confidence associated with the model's capacity to identify all objects of a particular class [44]. The trade-off between them is critical because increasing precision reduces the number of alarms generated by the system but increases the risk of missing some detections. In contrast, increasing recall may increase the number of alarms, including some false ones, to minimize missed detections. Object-detection models assign each detected object a confidence score [45]. These scores are used as thresholds for determining the validity of detections. By conducting a comparison analysis of precision and recall metrics across various confidence score thresholds, it is feasible to enhance the model's decision-making process and effectively monitor the effectiveness of the object-detection model over time. Figure 11 depicts a precision-confidence and a recall-confidence graph. The recall-confidence graph illustrates the correlation between recall and confidence thresholds within the context of a detection task. In addition, it provides valuable insights into the intrinsic trade-off between recall and precision during the adjustment of the confidence threshold. \begin{table}
\begin{tabular}{l l} \hline \hline
**Measure** & **Value (\%)** \\ \hline Sensitivity & 96.65 \\ Specificity & 99.57 \\ Precision & 97.07 \\ Negative Predictive Value & 99.57 \\ False Positive Rate & 0.42 \\ False Discovery Rate & 2.93 \\ False Negative Rate & 3.34 \\ F1 Score & 96.8 \\ Matthews Correlation Coefficient & 96.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall Performance Analysis of the Proposed Model. Figure 10: F1 confidence and precision–recall curve. Figure 11 shows that the model obtains a recall value of 96% at a very low threshold value. This implies that the model successfully detects positive cases across all classes. In a similar manner, the model demonstrates its robustness by achieving a maximum precision value of 99% when the confidence threshold is set to the highest value of 1.