## 4 Discussion
### Contributions and Limitations  
The proposed Ambient Detection method made significant contributions to improving object recognition reliability in construction operation monitoring through context-based analysis. This research results can overcome the limitations of existing single-object recognition by quantitatively modeling relationships between objects. Conventional appearance-based detection approaches often produce both misdetection and complete failure errors in complex scenes [66; 67]. In contrast, the relationship-based approach yields more precise judgments by capturing the semantic connections between operations. This approach is particularly useful in construction sites where object boundaries are ambiguous and visual similarities are high. The systematic integration of three relationship factors--distance, attribute, and spatial relationships--and the experimental validation of each factor's contribution represent a notable advancement. By clearly identifying the individual and combined effects of each relationship factor through Ablation Studies, this research established a robust theoretical framework for relationship-based object recognition. This framework can serve as a foundation for future research in relationship-based object recognition across various construction monitoring applications. Additionally, the proposed methodology has been implemented as a practical system capable of automatically monitoring compliance with concrete pouring height regulations. This directly contributes to improved quality management efficiency at construction sites. By replacing the subjectivity and inefficiency of existing manual inspection methods with an objective and accurate automated system, it provides scalability to monitor multiple pouring points simultaneously. However, several limitations exist in this study. The current model structure was validated using a diverse set of test images to determine appropriate relationship factor thresholds. However, for optimal performance in real-world applications, the model should be adjusted and fine-tuned using data that reflect the specific conditions of each construction site. Additionally, the complex model calculating distance, attribute, and spatial relationships has high computational complexity, potentially making real-time operation difficult on resource-constrained field equipment. Another significant limitation concerns the accuracy of height estimation, which is a core component of the pouring compliance monitoring system. This study did not perform quantitative validation of the estimated pouring heights, due to the lack of reference height data in the collected images. Specifically, this study employed the OpenPose algorithm for real-time 2D human pose estimation. OpenPose returns 14 key joint coordinates in our configuration, and we additionally calculated the midpoint between the neck and nose keypoints to use as a reference landmark for height calibration. This anatomical landmark sets the worker's height at approximately 175 cm and assigns the midpoint of the neck at a height of 1.5 m. The worker's height was set at 175 cm considering the average height of adult Korean males plus footwear. While the method of using the landmark between the head and neck as a 1.5 m reference to calibrate height according to worker behavior is appropriate, it has limitations in that it does not consider individual workers' physical conditions, which can introduce systematic measurement errors by universally applying the same pixel-to-meter conversion factor. When different workers are used as references, each individual's unique physical characteristics introduce different biases into the AI system's pixel size calculations. For example, when a worker with an actual neck height of 1.4 m appears at 300 pixels, the system calculates a conversion factor of 0.005 m/pixel (1.5 m/300 px), but the correct factor should be 0.0047 m/pixel (1.4 m/300 px), resulting in systematic overestimation of approximately 7%.