# Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV
## 5 Discussion  
This study presents an innovative BIM-integrated automated pipeline detection framework that leverages SAM for segmentation, DiffusionEdge for contour extraction, and Hu moment matching for structural comparison. Compared to conventional inspection methods that rely on manual assessments and rule-based feature extraction (e.g., Canny [12], HED [14]), the proposed approach significantly enhances detection robustness, automation, and adaptability to complex construction environments. While prior deep learning-based methods such as PipeNet [9] have improved defect identification, they remain computationally expensive and data-dependent. This study advances the field by integrating BIM projection refinement to ensure precise spatial alignment between as-built and as-designed conditions. The application of SAM in MEP component detection enhances segmentation accuracy, while DiffusionEdge outperforms traditional edge detection techniques, achieving a 3-5%. F-score improvement. Furthermore, Hu moment-based shape matching refines the comparison process, effectively mitigating perspective distortions and geometric inconsistencies encountered in previous methods such as OEF [32] and SE [13]. Despite these advancements, the proposed framework presents several limitations that warrant further research. The accuracy of BIM projection and SAM-based segmentation is highly contingent on input image quality, making the system susceptible to occlusions, lighting variations, and camera misalignment. Future studies should explore multi-view fusion techniques, such as integrating structured light scanning or SLAM-based spatial reconstruction, to mitigate data quality constraints. Additionally, the computational overhead associated with deep learning-based segmentation and contour extraction remains a concern, particularly for large-scale projects requiring real-time processing. Lightweightmodels such as PiDiNet [17] provide a potential solution, but optimizing the trade-off between efficiency and accuracy remains a challenge. Another limitation is the narrow scope of application, as this study focuses primarily on pipeline monitoring. Expanding the framework to encompass other MEP components, such as HVAC systems, electrical conduits, and structural reinforcements, would enhance its practical applicability. Furthermore, while the current system effectively identifies static deviations, dynamic construction monitoring requires time-series analysis and predictive modeling to track temporal changes and enable real-time anomaly detection and proactive intervention. Future research should focus on three key areas to further enhance the system's capabilities: multi-modal sensor fusion, adaptive calibration mechanisms, and real-time monitoring frameworks. Integrating heterogeneous sensor data (e.g., LiDAR, infrared thermography, and ultrasonic scanning) with deep learning could improve detection robustness and adaptability to diverse environments. Additionally, automated camera calibration using Structure-from-Motion (SfM) techniques would allow for dynamic parameter estimation, enhancing flexibility in varying construction conditions. Finally, transitioning from post-hoc analysis to real-time monitoring via reinforcement learning or adaptive filtering techniques could enable intelligent decision-making and proactive intervention. By advancing BIM-integrated computer vision methodologies, the construction industry can achieve higher levels of precision, automation, and intelligence in project management, ultimately setting new benchmarks for efficiency, quality assurance, and sustainability. Our study focuses on detecting MEP components and verifying their consistency with BIMs, without identifying or tracking individuals. Privacy protection techniques (e.g., blurring, masking, or encrypting sensitive image regions) ensure compliance with data privacy regulations. To mitigate privacy risks, we primarily use synthetic data from BIMs or controlled environments (e.g., unoccupied construction sites) for training and testing.