## 4 Discussion
### Future Research Directions  
To overcome the limitations identified in this study, we propose several directions for future research. To begin with, to address the real-time processing constraints, optimization of the relationship analysis algorithms and development of lightweight network structures are necessary [71; 76]. For systems to operate effectively in resource-constrained field environments, computational efficiency must be improved without sacrificing detection accuracy [77; 78; 79]. To enhance robustness across various environmental conditions, future work should integrate data augmentation techniques [80; 81; 82]. Additionally, domain adaptation methods that can respond to lighting changes, weather variations, and other environmental factors should also be incorporated [83; 84]. This would help maintain consistent detection performance across the diverse conditions encountered at construction sites [85]. To improve object recognition performance in extreme occlusion situations, techniques that utilize temporal context for tracking and methods that combine multi-view information should be considered [86]. These approaches could help maintain detection continuity even when physical connections between objects are temporarily obscured. Future research should also extend the Ambient Detection approach to encompass diverse objects and workflow across the entire concrete pouring process. While this study focused on pouring scene recognition, subsequent research should consider recognition models for equipment essential to ensuring concrete quality, such as vibrators. By detecting these objects comprehensively, the understanding of the entire process and the precision of quality assessment can be further enhanced. In addition, future research should extend the Ambient Detection approach to encompass diverse structural elements beyond concrete slabs, such as beams and foundation structures. These different structural elements present unique geometric characteristics and pouring constraints that require specialized adaptations of our framework. For beams, the narrower pouring area and vertical formwork create different monitoring challenges compared to horizontal slabs. Foundation structures, especially deep foundations, involve pouring in confined spaces with limited visibility. By adapting our relationship-based detection methodology to these various elements, we can develop a more comprehensive monitoring system. Such a system would be capable of identifying process anomalies specific to different structural elements that may compromise concrete quality and structural integrity. This would involve developing specialized relationship factors and ROI definitions tailored to the unique characteristics of each structural element and its corresponding pouring equipment configurations. The goal would be to create an integrated monitoring platform that tracks concrete quality across all structural elements throughout the construction project lifecycle. This expanded application would significantly enhance the practical utility of the Ambient Detection approach. It would provide construction managers with a unified system for maintaining concrete quality standards across diverse structural components. Such extensions would further contribute to addressing the industry-wide challenge of ensuring uniform concrete quality in increasingly complex construction projects. The integration of Large Language Models (LLM) with the detection system represents another promising direction [87; 88]. Such integration could enable simultaneous recognition of multiple operations and their communication to managers in natural language [89; 90; 91]. This would allow the creation of an integrated smart monitoring system that provides high-level information supporting decision-making in real-time to site managers, rather than merely quantifying object recognition results.