## 6 Conclusions  
This research provided a comprehensive evaluation of YOLOv10-based object detection models for five categories of heavy equipment--loaders, dump trucks, excavators, remicons, and cranes--using surveillance camera imagery from construction sites. In particular, the findings indicated that advanced transformer-based backbones within the YOLOv10 framework, especially Swin Transformer, achieved top-tier performance: YOLOv10-Swin Transformer attained AP50 = 89.72, AP50:95 = 66.71, IOU = 84.81, \(AP_{S}\) = 62.84, \(AP_{M}\) = 86.39, and \(AP_{L}\) = 92.53--outperforming other configurations. Meanwhile, alternative backbones such as ViT and PVT also demonstrated strong detection metrics but fell slightly behind Swin Transformer. Although smaller or visually less distinct machinery posed lingering difficulties, the overall gains in detection accuracy highlight YOLOv10's potential for enhanced safety monitoring on construction sites.  
Nonetheless, the reduced accuracy observed on certain external datasets underscores the need for broader training data and refined domain adaptation, ensuring robust generalization across varied environments. Future avenues include expanding the dataset to encompass additional contexts and equipment types, incorporating multimodal data sources, and further optimizing real-time performance for practical, large-scale implementation in construction settings.  
Conceptualization, S.W. and J.K. ([PERSON]); methodology, S.W.; software, I.E.; validation, I.E., I.E. and J.K. ([PERSON]); formal analysis, J.K. ([PERSON]); investigation, J.K. ([PERSON]); resources, I.E.; data curation, I.E., S.W. and I.E.; writing--original draft preparation, S.W. and J.K. ([PERSON]); writing--review and editing, J.K. ([PERSON]) and J.K. ([PERSON]); visualization, J.K. ([PERSON]); supervision, S.W.; project administration, S.W.; funding acquisition, J.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (grant number RS202400356697).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Detector** & **Architecture** & **AP50** & **AP50:95** & **IOU** & \(AP_{S}\) & \(AP_{M}\) & \(AP_{L}\) & **FPS** \\ \hline \multirow{4}{*}{Faster R-CNN} & ResNet-101 & 86.13 & 63.24 & 82.11 & 58.22 & 83.15 & 90.47 & 32 \\  & ResNet-152 & 87.67 & 64.85 & 83.27 & 59.13 & 84.64 & 91.15 & 33 \\  & MobileNetv3 & 85.24 & 62.01 & 81.94 & 57.75 & 82.41 & 89.83 & 30 \\  & ResNet-101 & 86.44 & 62.90 & 82.29 & 58.42 & 83.64 & 90.84 & 35 \\ SSD500 & ResNet-152 & 87.88 & 64.13 & 83.71 & 58.98 & 84.96 & 91.32 & 32 \\  & MobileNetv3 & 84.72 & 61.24 & 81.57 & 56.93 & 81.74 & 89.15 & 28 \\  & ResNet-101 & 86.03 & 63.12 & 82.03 & 58.07 & 83.13 & 90.26 & 31 \\ R-FCN & ResNet-152 & 87.42 & 64.52 & 83.62 & 59.02 & 84.52 & 90.91 & 32 \\  & MobileNetv3 & 85.57 & 62,44 & 81.73 & 57.68 & 82.23 & 89.53 & 33 \\ Proposed method & 89.72 & 66.71 & 84.81 & 62.84 & 86.39 & 92.53 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of applying the trained model to test set.