Precise Positioning Technology for Tunnel Shield Segment Assembly Based on Machine Vision and Neural Networks

High precision positioning of tunnel shield section assembly has become one of the key technical problems in the construction process with the rapid development of the tunnel shield construction technology. To this end, this paper discusses a technical model for tunnel shield section precise positioning based on machine vision and neural network. The model is based on existing visual recognition algorithms and deep learning methods, applies Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) to achieve the three-dimensional spatial information of the shield section obtained from the multiview image data, and is used for real-time positioning and error correction in the accurate assembly process. The innovative point is the strategy of local feature fusion and adaptive learning, which enables the model to deal with interference factors such as illumination change and object deformation in complex environment. Based on the actual tunnel construction with the model of experimental verification, the results are verified that the model is significantly superior to the traditional method in positioning accuracy and stability, the positioning error of about a 30% decrease, and real time and adaptability are significantly improved.