## 5 Results, and Discussion
### Model Evaluation in Test Images  
The best-performing model was YOLOv10-Swin Transformer, achieving 89.72 for AP50, 66.71 for AP50:95, 84.81 for IOU, 62.84 for \(\mathit{AP_{S}}\), 86.39 for \(\mathit{AP_{M}}\), and 92.53 for \(\mathit{AP_{L}}\) on average. As depicted in Figure 9, each class's performance under these metrics aligned closely with results observed on the validation set, indicating minimal overfitting. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Architecture: ViT & & & & & & \\ \hline Class & AP50 & AP50:95 & IOU & \(\mathit{AP_{S}}\) & \(\mathit{AP_{M}}\) & \(\mathit{AP_{L}}\) \\ Loader & 88.24 & 65.47 & 82.63 & 61.02 & 84.52 & 90.82 \\ Dump Truck & 90.15 & 68.39 & 85.22 & 63.77 & 88.57 & 92.41 \\ Excavator & 91.03 & 69.42 & 86.35 & 65.90 & 89.92 & 93.51 \\ Remicon & 84.72 & 62.49 & 80.15 & 59.11 & 81.26 & 89.67 \\ Crane & 87.51 & 64.81 & 82.43 & 60.25 & 83.22 & 90.42 \\ Average & 88.33 & 66.12 & 83.36 & 62.01 & 85.50 & 91.37 \\ \hline \hline Architecture: Swin Transformer & & & & & & \\ \hline Class & AP50 & AP50:95 & IOU & \(\mathit{AP_{S}}\) & \(\mathit{AP_{M}}\) & \(\mathit{AP_{L}}\) \\ Loader & 89.73 & 66.81 & 83.74 & 61.10 & 86.71 & 92.12 \\ Dump Truck & 91.34 & 69.48 & 86.42 & 65.27 & 89.37 & 94.52 \\ Excavator & 92.44 & 70.22 & 87.69 & 67.01 & 90.52 & 94.91 \\ Remicon & 86.14 & 63.92 & 81.60 & 61.29 & 82.71 & 90.27 \\ Crane & 89.18 & 65.93 & 84.40 & 63.42 & 84.91 & 91.77 \\ Average & 89.77 & 67.27 & 84.77 & 63.62 & 86.84 & 92.72 \\ \hline \hline Architecture: PVT & & & & & & \\ \hline Class & AP50 & AP50:95 & IOU & \(\mathit{AP_{S}}\) & \(\mathit{AP_{M}}\) & \(\mathit{AP_{L}}\) \\ Loader & 88.52 & 65.91 & 82.84 & 60.98 & 85.12 & 91.07 \\ Dump Truck & 89.83 & 67.13 & 84.57 & 62.01 & 88.03 & 92.72 \\ Excavator & 90.16 & 68.03 & 85.42 & 63.95 & 89.24 & 93.10 \\ Remicon & 85.91 & 62.10 & 80.53 & 58.55 & 81.73 & 88.30 \\ Crane & 87.73 & 64.04 & 82.32 & 59.60 & 83.52 & 90.14 \\ Average & 88.43 & 65.44 & 83.14 & 61.02 & 85.53 & 91.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Class-wise performance of augmented models using different architectures. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Architecture** & **Mean** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline ViT & 34.3 & 34 & 34 & 34.2 & 34.5 & 35 \\ Swin Transformer & 35.4 & 34 & 34 & 35.5 & 35.8 & 36 \\ PVT & 36.6 & 34 & 35 & 35.3 & 36.7 & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of inference time (FPS). Figures 10 and 11 present sample images illustrating high and low detection accuracies, respectively. In Figure 10a, three excavators are operating under well-lit conditions within an excavated pit, whose walls are reinforced by temporary beams and surrounded by construction materials and soil piles. High-rise buildings are visible in the background, indicating an urban context, and all excavators in this scene were successfully detected. In Figure 10b, one excavator is offloading soil into a nearby dump truck, while another excavator is working in the background; both are correctly labeled \"excavator\" with the dump truck enclosed by a corresponding bounding box. Two workers, clad in reflective pests, stand at the edge of the pit observing the activity, which includes substantial excavation, supporting beams, and a relatively open construction environment. Figure 10: Sample images detected with 100% accuracy. Figure 9: Class-wise performance on test set. In Figure 10(a), an excavator is operating near a pile of rebars, with one dump truck parked in the foreground and another positioned farther back. A crane is also visible in the background but is incorrectly detected as a \"remicon\". The site itself appears expansive, encircled by retaining walls, stacked construction materials, and partially completed infrastructure. Meanwhile, Figure 10(b) depicts an open excavation area supported by beams, with large piles of earth and high-rise buildings in the distance--indicative of an urban setting under clear, bright conditions.