Relationship-Based Ambient Detection for Concrete Pouring Verification: Improving Detection Accuracy in Complex Construction Environments - Seungwon Yang; Hyunsoo Kim (Applied Sciences)
Efficient monitoring of concrete pouring operations is critical for ensuring compliance with construction regulations and maintaining structural quality. However, traditional monitoring methods face limitations such as overlapping objects, environmental similarities, and detection errors caused by ambiguous boundaries. This study proposes an Ambient Detection-based Monitoring Framework that enhances object detection by incorporating contextual relationships between objects in complex construction environments. The framework employs the You Only Look Once version 11 (YOLOv11) algorithm, addressing issues of boundary ambiguity and misrecognition through relational analysis. Key components including Distance Relationship (<semantics>DR</semantics>), Attribute Relationship (<semantics>AR</semantics>), and Spatial Relationship (<semantics>SR</semantics>) allow the system to quantitatively evaluate contextual associations and improve detection accuracy. Experimental validation using 232 test images demonstrated a 12.07% improvement in detection accuracy and a 71% reduction in false positives compared with baseline YOLOv11. By automating the monitoring process, the proposed framework not only improves efficiency but also enhances construction quality, demonstrating its adaptability to diverse construction scenarios.