Large-Scale Civil Engineering Structure Deformation Monitoring Research Based on Image Recognition

Large-scale civil engineering structures may experience deformations during construction and use due to various reasons, affecting the safety and service life of the structures. The application of image recognition technology in the field of civil engineering has promoted the research and development of related technologies, providing new technical means for the monitoring and evaluation of civil engineering structures. Existing image recognition methods may be affected by factors such as lighting, occlusion, and image quality when dealing with large-scale civil engineering structure deformation monitoring, resulting in reduced recognition accuracy. Therefore, this study conducts research on large-scale civil engineering structure deformation monitoring based on image recognition. Traditional civil engineering structure deformation detection methods are presented. A simple and intuitive curve expression is used to describe the deformation characteristics of civil engineering structures, and GCN is used to mine the feature information between adjacent feature points and long-distance related points to improve prediction performance. A graph convolution prediction module and a geometric auxiliary prediction module are set up for the constructed prediction model, and the setting objectives and structural principles of the two modules are explained. In response to the challenges of extracting large-scale civil engineering structure deformation curves, an automatic extraction method based on deep learning networks is proposed, achieving high-precision recognition and extraction of civil engineering structure deformation curves. Experimental results validate the effectiveness of the proposed method.