## 1 Introduction
During pouring tasks, the pouring boom head and the concrete are logically and physically connected, yet current systems detect them independently [45]. This results in frequent misrecognition, where visually similar objects are falsely identified as concrete, or actual pouring concrete is not detected at all [46; 47]. Second, the systems struggle with visual ambiguity [48; 49]. Construction materials often share similar visual characteristics, which leads to boundary ambiguity and significantly reduces recognition accuracy [50]. For instance, the concrete discharged from the pouring boom and the already poured surface concrete have nearly identical color and texture. Conventional object detection algorithms are not well-equipped to distinguish between such visually similar but functionally distinct elements [51; 52]. This challenge is intensified in congested construction environments where multiple objects operate in close proximity [53; 54]. Such conditions result in occlusions and complex visual scenes that significantly reduce the recognition accuracy of existing detection methods [55; 56; 57; 58]. Architectures such as YOLOv11 are inherently focused on learning individual object features, without incorporating contextual or relational information [59; 60]. As a result, it cannot reliably differentiate between concrete actively being poured and concrete already poured onto the floor surface, despite their distinct functional roles in the construction process [61]. The misdetections and complete failure caused by this limitation significantly impair the accuracy of measuring critical parameters such as pouring height [62]. To address these critical issues, our study introduces an ambient detection approach that analyzes spatial and visual relationships between objects in construction environments. The fundamental difference between conventional YOLO-based detection and the proposed Ambient Detection lies in their approach to object recognition. Traditional YOLO algorithms rely solely on individual object features, analyzing each detected object independently without considering its contextual relationships with surrounding elements. This single-object recognition approach often fails in construction environments where objects share similar visual characteristics or exist in complex spatial arrangements. In contrast, Ambient Detection enhances the baseline YOLO detection by incorporating three critical relationship factors that analyze contextual associations between objects. Specifically, these factors include: (1) Distance Relationship--quantifying the physical proximity between the pouring boom and concrete objects, (2) Attribute Relationship--measuring visual distinctiveness through feature vector analysis, and (3) Spatial Relationship--evaluating positional constraints that reflect the physical laws of concrete pouring operations. These relationship factors are combined through a weighted mathematical formulation rather than additional neural networks, ensuring computational efficiency while maintaining interpretability. The superior performance of Ambient Detection stems from its ability to leverage contextual information that conventional YOLO systems ignore. By analyzing inter-object relationships, the system can distinguish between actively poured concrete and already placed surface concrete, even when they appear visually identical. This relationship-based approach directly addresses the two primary limitations of YOLO-based systems: boundary ambiguity caused by visual similarity and misdetection due to lack of contextual understanding. By integrating contextual information and modeling object relationships, this approach aims to overcome the inherent limitations of single-object recognition. It also significantly enhances the monitoring accuracy for concrete pouring operations. This study introduces Ambient Detection technology, an approach designed to enhance the accuracy of monitoring concrete pouring operations by analyzing the relationships between objects.