# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 5 Conclusions  
This study evaluated the detection performance of YOLOv8 and SAMURAI for monitoring workers in masonry tasks, specifically in brick transportation and brick laying. The results demonstrate that YOLOv8 provides fast real-time detection, making it well-suited for dynamic environments where immediate monitoring is necessary. However, its reliance on frame-by-frame detection leads to a significant decline in accuracy under occlusion, particularly in cases of prolonged or severe obstructions. In contrast, SAMURAI maintains a high detection accuracy even in occlusion-heavy conditions due to its memory-based tracking mechanism, but its higher computational demands limit its real-time applicability.  
Through this comparative analysis, it was observed that each model has distinct advantages and limitations depending on the work environment and occlusion conditions. YOLOv8 performs effectively when occlusion is minimal, ensuring rapid inference, while SAMURAI is more robust in environments with frequent occlusion, allowing for the more stable tracking of workers despite visual obstructions. These findings highlight the need to select the appropriate detection model based on the specific requirements of the construction site, balancing the need for speed and accuracy according to the operational demands.  
Furthermore, this study proposes a hybrid detection approach that integrates YOLOv8's speed with SAMURAI's robustness against occlusion. While the potential benefits of such an approach have been outlined, further research is required to develop and validate an integrated framework that optimizes both real-time performance and detection accuracy. Additionally, future studies should focus on evaluating the models under diverse environmental conditions, including varying lighting, complex backgrounds, and different types of occlusions, to ensure broader applicability in real-world construction settings.  
By addressing the challenges of worker detection during the performance of masonry tasks, this study contributes to improving productivity monitoring and safety management on construction sites. The findings provide a foundation for developing more adaptive and reliable vision-based monitoring systems, ensuring their effectiveness in diverse and dynamic construction environments.  
Conceptualization, S.Y. and H.K.; methodology, S.Y. and H.K.; validation, S.Y. and H.K.; investigation, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y. and H.K.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by a grant (RS-2022-00143493) from a Digital-Based Building Construction and Safety Supervision Technology Research Program, funded by the Ministry of Land, Infrastructure and Transport of the Korean Government.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Institutional Review Board of Dankook University (DKU 2020-09-027, date of approval 14 September 2020).  
**Informed Consent Statement:** Written informed consent was obtained from the participant(s) to publish this paper.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.