## 3 Results  
### Performance Analysis of the Trained Model  
After going through 60 runs, the model was evaluated to see how it performed. Main test scores like precision, recall, and mean average precision (mAP), were calculated to see \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Time of the Shift**} & \multicolumn{1}{c}{**25,560**} & \multicolumn{1}{c}{**25,680**} & \multicolumn{1}{c}{**25,800**} & \multicolumn{1}{c}{**26,040**} & \multicolumn{1}{c}{**26,160**} & \multicolumn{1}{c}{**26,220**} & \multicolumn{1}{c}{**26,280**} \\ \multicolumn{1}{c}{**(s)**} & & & & & & & & \\ \hline \multirow{9}{*}{Truck IDs} & 3 & 0 & 0 & 0 & 3 & 0 & 0 & 0 \\  & 5 & 0 & 5 & 0 & 0 & 0 & 5 & 0 \\  & 6 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 9 & 0 & 0 & 0 & 9 & 0 & 0 & 0 \\  & 10 & 0 & 0 & 0 & 0 & 0 & 10 & 0 \\  & 11 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 12 & 0 & 0 & 12 & 0 & 0 & 0 & 0 \\  & 13 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 14 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 25 & 0 & 0 & 0 & 0 & 25 & 0 & 0 \\ \hline \multirow{9}{*}{Excavator} & 0 & 138 & 18 & 2 & 36 & 116 & 56 & 76 \\  & 1 & 4268 & 4148 & 4028 & 3788 & 3668 & 3608 & 3548 \\ \cline{1-1}  & 2 & 3633 & 3513 & 3393 & 3153 & 3033 & 2973 & 2913 \\ \cline{1-1} (s) & 3 & 93 & 72 & 33 & 36 & 71 & 11 & 34 \\ \cline{1-1}  & 4 & 91 & 222 & 102 & 93 & 36 & 78 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example dataset of the trucks and excavator ready times. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Time of the Shift**} & \multicolumn{1}{c}{**25,560**} & \multicolumn{1}{c}{**25,680**} & \multicolumn{1}{c}{**25,800**} & \multicolumn{1}{c}{**26,040**} & \multicolumn{1}{c}{**26,160**} \\ \hline \multirow{9}{*}{Truck IDs} & 1 & 99 & 99 & 99 & 99 & 99 \\  & 2 & 99 & 99 & 99 & 99 & 99 \\  & 3 & 99 & 99 & 99 & 1 & 99 \\  & 4 & 99 & 99 & 99 & 99 & 99 \\  & 5 & 99 & 1 & 99 & 99 & 99 \\  & 6 & 99 & 99 & 99 & 9  
how well the model detects \"truck\" and \"excavator\". Both check and test datasets were used to make sure the model's guesses were strong with new data. Figure 6 shows the model performance improvements during training and validation. The graphs for Train/box_loss, Train/cls_loss, and Train/dfl_loss show that the box fits, hits the right class, and edge details performed better over time. Similarly, validation graphs Val/box_loss, Val/cls_loss, and Val/dfl_loss demonstrate consistent reductions in errors, confirming generalization to unseen data. The Metrics/precision(B) and Metrics/recall graphs show that the model's predictions became more stable and sharper. Also, the Metrics/mAP50(B) graph shows mean average precision close to 95%, while the Metrics/mAP50-95(B) graph shows strong scores across different test levels. All these points prove the model works well and can be trusted. Table 6 delineates the evaluation metrics for a test set comprising 124 images and 289 instances, demonstrating the model's efficacy with an overall precision of 0.966, recall of 0.978, mAP50 of 0.99, and mAP50-95 of 0.911 for the aggregated \"all\" class, while also providing detailed outcomes for the \"excavator\" and \"truck\" categories. The results underscore the model's robust performance, with the \"truck\" category exhibiting the highest precision (0.982) and mAP50 (0.993), and the \"excavator\" category maintaining a commendable precision (0.95) and recall (0.975). Representative instances from this test set are depicted in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Images** & **Instances** & **Precision** & **Recall** & **mAP50** & **mAP50–95** \\ \hline all & 124 & 289 & 0.966 & 0.978 & 0.99 & 0.911 \\ \hline excavator & 119 & 137 & 0.95 & 0.975 & 0.986 & 0.912 \\ \hline truck & 106 & 152 & 0.982 & 0.98 & 0.993 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test set evaluation metrics. Figure 6: Object detection accuracy metrics of the trained model. By using these tests, the overall reliability and effectiveness of the trained model can be evaluated in real-world applications.