Vision-based high-precision intelligent monitoring for shield tail clearance

Real-time shield tail clearance measurement and monitoring is a key task during shield tunneling construction. The shield tail clearance measurement and monitoring technology development is still in its infancy, the current methods are mainly designed manually based on intuition. In order to fill the gap between the requirement of shield tail clearance measurement and monitoring and the limitations of the current methods, this paper systematically studies the existing mechanisms related to shield tail clearance measurement and monitoring, and develops a high-precision intelligent monitoring system for shield tail clearance. The proposed monitoring system includes four components: 1) two types of shield tail clearance calculation models, 2) the integrated hardware of the monitoring system which is composed of a data acquisition unit, a signal transmission unit and a control unit, 3) the region of interest (ROI) extraction method based on deep neural network, and the image processing algorithms for image enhancement and feature extraction, 4) the custom-developed software built on mature integrated development environment (IDE). After the calculation model of shield tail clearance is established, the system uses monitoring devices equipped with industrial cameras to obtain the on-site image, and then applies image processing technologies along with deep learning approach to extract the key features, which are brought into the model to calculate the values of shield tail clearance, finally displays these values and simulates the current tunneling attitude of the shield machine in real time. The experimental results show that the system proposed in this paper achieves the goal of high precision measuring and real-time monitoring of the shield tail clearance.