Advances in Constructional Engineering

To address the challenges of high detection difficulty and low defect recognition efficiency in intelligent building weak current shaft equipment, this paper proposes a defect detection method based on an improved YOLOv7. By introducing attention mechanisms and multi-scale feature fusion techniques, the model's ability to capture fine details of weak current shaft equipment is enhanced, achieving high-precision and real-time defect identification. Experimental results demonstrate that this method outperforms traditional algorithms in both detection accuracy and speed, showing strong practical engineering value.

Intelligent building; Weak current shaft equipment; Defect detection; YOLOv7; Deep learning