# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 4 Discussion
### Contribution and Limitations  
The comparative analysis of YOLOv8 and SAMURAI highlights the balance between detection speed and accuracy. YOLOv8 is highly effective in environments with minimal occlusion, ensuring fast detection with lower computational costs. However, its frame-by-frame processing makes it vulnerable to detection failures under occlusion. In contrast, SAMURAI excels in environments with frequent occlusions, maintaining higher accuracy even in challenging detection scenarios. Quantitatively, SAMURAI demonstrates approximately 1.3 times higher average detection accuracy compared to YOLOv8 across all occlusion conditions, though at the cost of 2.5-3.5 times slower processing speed. These differences suggest that a hybrid detection approach combining YOLOv8's speed and SAMURAI's occlusion resilience could serve as a viable alternative for optimizing perfor \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **YOLOv8** & **SAMURAI** \\ \hline Average Accuracy (\%): Cases 1, 2, and 3�����⃝ & 74.55 & 95.11 \\ Precision (\%) & 97.00 & 96.50 \\ Recall (\%) & 98.00 & 94.80 \\ F1-Score & 0.97 & 0.95 \\ Processing Speed (FPS) & 28–32 & 9–12 \\ Time to Process 15s Video: Carrying Bricks (s) & 0.55–0.65 & 1.4–1.7 \\ Time to Process 60s Video: Brick Laying (s) & 2.2–2.5 & 5.8–6.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between YOLOv8 and SAMURAI. mance across diverse construction environments. Moreover, this comparison contributes to the decision-making process in selecting an appropriate monitoring algorithm based on the specific requirements of construction sites. By analyzing the strengths and weaknesses of both models, this study provides insights that can guide the development of optimized monitoring strategies tailored to the specific requirements of construction site operations. The implementation of accurate worker tracking systems has significant implications for construction productivity and safety management [58; 59]. Enhanced detection accuracy, particularly in occlusion-heavy environments, enables more reliable productivity measurements, facilitates data-driven performance evaluations, and supports proactive safety monitoring by maintaining continuous worker visibility [15; 58]. For instance, in masonry construction, accurate tracking can help identify inefficient work patterns, optimize material handling procedures, and reduce safety risks associated with blind spots. From a practical standpoint, integrating SAMURAI into existing monitoring systems may influence project workflows, budget allocation, and system scalability. For example, deploying SAMURAI may require more robust hardware infrastructure, such as high-performance GPUs, and increased maintenance due to its computational demands. These factors can affect cost-efficiency and scalability, especially in large or resource-constrained projects. Nonetheless, in environments where precise worker tracking is critical, the enhanced detection performance may justify the additional investment by improving safety outcomes and operational oversight. However, study has several limitations. The experiments were conducted in a controlled environment, meaning that real-world variations in lighting, background complexity, and unpredictable worker movements could introduce variability in detection performance. The focus on masonry work limits the generalizability of the findings to other construction tasks, such as steel frame assembly or excavation, which may present different occlusion patterns and movement dynamics. Additionally, the evaluation focused on specific occlusion cases, which may not fully capture the diversity of real-world occlusion scenarios in construction sites. Moreover, the occlusion levels in this study were defined using IoU thresholds. However, IoU alone may not fully capture the dynamic nature and complexity of occlusion in real-world environments. Future studies should consider incorporating additional metrics or qualitative assessments to improve occlusion characterization.