## 1 Introduction  
In recent years, sensor-based technologies for analyzing workers' movements and automated monitoring systems for real-time evaluation of construction status have gained significant attention in construction sites [1, 2, 3, 4]. Among these technologies, automated monitoring systems have emerged as a key solution for overcoming the subjectivity and inefficiency of traditional human-centered supervision methods [5, 6]. Such systems contribute to improved overall construction quality and safety by objectively and accurately evaluating compliance with various work regulations [7, 8]. In particular, compliance with regulations such as concrete pouring height and compaction intervals directly impacts the final quality of concrete structures [9]. Noncompliance with these standards may lead to concrete material segregation, potentially reducing the structural strength and durability of the completed construction [10]. In current construction practices, verification of work regulation compliance is primarily conducted through visual inspection by supervisors [11, 12]. This approach presents several limitations that reduce its effectiveness and reliability [13]. Concrete pouring operations often involve multiple simultaneous tasks, making it practically difficult for a small number of supervisors to effectively monitor all points at once [14]. Additionally, the current approach relies heavily on subjective judgment by supervisors, presenting challenges in accurately measuring quantitative criteria such as pouring height [15; 16]. The complexity of construction environments and combined with the rapid pace of operations complicates consistent and continuous monitoring through traditional methods [17; 18]. To address these issues deep learning-based object recognition technologies have been utilized in construction sites to automatically identify various work progress situations [19; 20; 21]. These include rebar arrangement verification, formwork installation progress tracking, masonry pattern analysis, jack-support installation spacing measurement, and heavy equipment operation monitoring [13; 22; 23; 24]. The advancement of real-time object detection algorithms like You Only Look Once version 11 (YOLOv11) has significantly increased the feasibility of field applications [6; 25; 26; 27; 28; 29]. YOLOv11 and similar algorithms provide real-time object detection capabilities that are well-suited for construction environments [18; 30; 31; 32]. These technologies offer improved accuracy and efficiency compared to traditional visual monitoring methods by supervisors [33; 34]. They help track work status in real-time and support construction managers in making informed decisions [35]. Object recognition technology has proven particularly useful in work progress tracking and quality management [24; 36]. For instance, object recognition in masonry work can be utilized to check the overall situation and progress of the bricklaying activities through image analysis [24]. In jack-support installation, it allows for verifying the installation locations continuously without manual checks [23]. Safety monitoring applications have also been developed using these technologies [26; 28]. Systems can now automatically monitor the safety distance between workers and equipment, helping prevent accidents and ensure compliance with safety regulations [37; 38]. The ability to detect and track multiple objects simultaneously makes these systems effective even in dynamic construction environments [39; 40; 41]. Despite these advancements, YOLO-based automated monitoring systems for construction sites present several critical limitations when deployed in real-world environments [42]. In the context of concrete pouring operations, these systems encounter two major issues. First, they rely on single-object recognition methods that fail to consider the inherent relationships between objects [43; 44].