Integration of Computer Vision and BIM in Safety Monitoring of Water Conservancy Projects

This paper proposes a water conservancy project safety monitoring algorithm based on deep learning and computer vision and BIM data fusion. The algorithm uses the advantages of CNN in image recognition to process the images of water conservancy projects and extract potential safety hazard features from them. By integrating with BIM (Building Information Model) data, a comprehensive analysis and evaluation of the project safety status from multiple dimensions is achieved. Specifically, the algorithm can monitor and automatically warn of possible safety hazards in real time by comparing image feature extraction with BIM data. In the experimental part, this paper uses the on-site images and BIM model data of actual water conservancy projects for testing. The results show that the safety monitoring system based on the integration of computer vision and BIM has a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 0 \%}$</tex> higher detection accuracy and a 15 % lower response time than traditional monitoring methods. At the same time, the algorithm has high stability and scalability when processing complex field data, which can effectively improve monitoring efficiency and reduce manual intervention. Through experimental verification, this system provides a more intelligent and efficient solution for the safety monitoring of water conservancy projects.