# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
###### Abstract  
This study evaluates the performance of You Only Look Once version 8 (YOLOv8) and a SAM-based unified and robust zero-shot visual tracker with motion-aware instance-level memory (SAMURAI) for worker detection in masonry construction environments under varying occlusion conditions. Computer vision-based monitoring systems are widely used in construction, but traditional object detection models struggle with occlusion, limiting their effectiveness in real-world applications. The research employed a structured experimental framework to assess both models in brick transportation and brick laying tasks across three occlusion levels: non-occlusion, partial occlusion, and severe occlusion. Results demonstrate that while YOLOv8 processes frames 2.5 to 3.5 times faster (28-32 FPS versus 9-12 FPS), SAMURAI maintains significantly higher detection accuracy, particularly under severe occlusion conditions (92.67% versus 52.67%). YOLOv8's frame-by-frame processing results in substantial performance degradation as occlusion severity increases, whereas SAMURAI's memory-based tracking mechanism enables persistent worker identification across frames. This comparative analysis provides valuable insights for selecting appropriate monitoring technologies based on specific construction site requirements. YOLOv8 is suitable for construction environments characterized by minimal occlusions and a high demand for real-time detection, whereas SAMURAI is more applicable to scenarios with frequent and severe occlusions that require the sustained tracking of worker activity. The selection of an appropriate model should be based on an initial assessment of environmental factors such as layout complexity, object density, and expected occlusion frequency. The findings contribute to the advancement of more reliable vision-based monitoring systems for enhancing productivity assessment and safety management in dynamic construction settings. m ac acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm acm ac acm acm ac acm ac acm ac acm ac acm ac acm ac ac acm ac ac acm ac  
Therefore, computer vision-based monitoring systems have been widely adopted in construction to automate worker activity tracking [10; 11; 12], productivity assessment [13; 14], and safety monitoring [15; 16; 17; 12; 18]. These systems leverage deep learning-based object detection and tracking algorithms to identify workers [17; 19; 20], analyze movement patterns [21; 22], and evaluate task efficiency [23; 24]. Among these, You only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Single Shot Multibox Detector (SSD) are commonly used models, offering real-time detection capabilities that facilitate the continuous monitoring of worker behavior in dynamic construction environments [25; 26; 27; 28]. Computer vision technologies have been utilized in construction for various monitoring applications, including safety compliance verification [29; 30; 31], progress tracking [32; 33; 34; 35; 36], and automated productivity analysis [37; 38]. For instance, several studies have employed YOLO-based detection systems to identify workers wearing personal protective equipment (PPE) and to monitor compliance with safety regulations [39; 40]. Additionally, motion analysis and pose estimation techniques have been integrated into construction site monitoring to evaluate worker posture, fatigue levels, and ergonomic risks [41; 42; 43]. Other research has explored the automated recognition of construction tasks, enabling productivity assessments based on detected worker actions and movements [44; 45]. Despite these advancements, existing object detection models primarily rely on frame-by-frame image processing, which presents challenges in highly dynamic and cluttered construction environments [15; 46; 47; 48]. Workers frequently interact with materials, tools, and other workers, resulting in occlusion scenarios that disrupt tracking continuity [49; 50; 51].