## 2 Materials and Methods
### Image Detection with YOLO Model
A truck is considered present in the loading area upon entry; however, if its duration within the geofence is less than 15 s--indicating a mere passage or similar occurrence--it is not recorded as being in the area. Readiness of the excavator is set by looking at three clear cases. These cases think about whether trucks are there and how many are in that space:  
* No trucks in the green area: If the green area is devoid of trucks, the excavator is deemed ready for operation. In this scenario, the ready time is assigned a value of zero seconds, indicating immediate availability for new assignments. * One truck in the green area: When a single truck is present within the green area, the excavator's ready time corresponds to the loading time of the truck. For the case study, this loading time is standardized to 300 s.
* Multiple trucks in the green area: If more than one truck occupies the green area, the additional trucks are classified as being \"in queue\". In this case, the ready time of the excavator is incrementally extended by the sum of the maneuvering and loading times \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Batch Size & 16 \\ Optimizer & AdamW \\ Learning rate & 0.01 \\ Weight decay & 0.0005 \\ Momentum & 0.937 \\ Drop out & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The hyperparameters of the trained model. Figure 3: Object tagging on the Roboflow platform. for each queued truck. This approach accounts for the additional delays caused by processing the queued trucks sequentially. This classification method helps calculate when an excavator will be ready. It looks at how excavators and trucks move and work together, showing real mining operations. At the same time, the model spots trucks at dumps to check which ones need new tasks. This fast-detect skill lets us add computer vision checks into the setup, making the truck team run smoothly and fast. Figure 4 illustrates four distinct scenarios of a mining operation, providing a comprehensive depiction of excavator availability and operational dynamics within the system. In Figure 4a, the excavator is idle, with no trucks present in the designated waiting area (green spot). Figure 4b depicts an active loading operation, where a truck occupies the green spot while the excavator is engaged in loading. Figure 4c highlights a queuing scenario, where a truck is waiting due to congestion in the green spot, demonstrating the impact of multiple trucks occupying the area. Figure 4d presents a truck at a dump site, requiring immediate dispatch to an available excavator. Together, these images offer a clear visualization of how excavator utilization fluctuates based on truck movements. Additionally, Figure 5 showcases a processed image with confidence levels assigned to detected trucks and excavators, further illustrating the effectiveness of the detection system. Additionally, Figure 5 showcases a processed image with confidence levels of 100%. Figure 4: Different scenarios of mining operations: (**a**) Scenario 1 (no trucks in the green area); (**b**) Scenario 2 (one truck in the green area); (**c**) Scenario 3 (multiple trucks in the green area); and (**d**) truck on a dump site. levels assigned to detected trucks and excavators, further illustrating the effectiveness of the detection system. In open-pit mining, the processed images provide a comprehensive dataset tracking important details. This covers production rates of the excavators, the number of empty trucks at dump sites, and the length of time their availability for new assignments. After that, this dataset is applied in a particular model meant to maximize the assignment of empty trucks to excavators. The model seeks to lower queue times and increase the general effectiveness of mining operations by considering elements including when excavators are ready, where trucks are parked, and how much work the excavators are performing.