In recent years, mobile laser measurement systems have markedly enhanced the capabilities of deformation detection and defect identification within metro tunnels, attributed to their superior efficiency, precision, and versatility. Nevertheless, challenges persist, including substantial equipment costs, inadequate after-sales support, technological barriers, and limitations in customization. This paper develops a mobile laser measurement system that has been specifically developed for the purpose of detecting deformation in metro tunnels. The system integrates multiple modules, comprising a rail inspection vehicle, a three-dimensional laser scanner, an odometer, and an inclinometer, to facilitate multi-sensor temporal synchronization. By leveraging data from the inclinometer and odometer, the system performs point cloud coordinate corrections and three-dimensional linear reconstructions. Experiments conducted on the Xuzhou Metro validate the reliability and stability of the system, demonstrating its capability to meet routine deformation detection requirements. To improve deformation detection utilizing point cloud data, a pre-processing algorithm has been proposed, which incorporates point cloud denoising, centerline calculation, roadbed removal, and relative positioning based on mileage. Disturbed points are systematically identified and eliminated, while the convergence of tunnel sections and inter-ring misalignment are evaluated through ellipse fitting. Furthermore, to address encroachments upon tunnel locomotive limits, encroachment points and associated information are identified using the ray method. In conclusion, the proposed mobile laser measurement system offers an efficient and reliable solution for metro tunnel deformation detection, with significant potential for broader applications and future advancements.
Remote sensing change detection (CD) identifies changes in each pixel of certain classes of interest from a set of aligned image pairs. It is challenging to accurately identify natural changes in feature categories due to unstructured and temporal changes. This research proposed an effective bi-temporal remote sensing CD comprising an encoder that could extract multiscale features, a decoder that focused on semantic alignment between temporal features, and a classification head. In the decoder, we constructed a new convolutional attention structure based on pre-generation of depthwise-separable change-salient maps (PDACN) that could reduce the attention of the network on unchanged regions and thus reduce the potential pseudo-variation in the data sources caused by semantic differences in illumination and subtle alignment differences. To demonstrate the effectiveness of the PDA attention structure, we designed a lightweight network structure for encoders under both convolution-based and transformer architectures. The experiments were conducted on a single-building CD dataset (LEVIR-CD) and a more complex multivariate change type dataset (SYSU-CD). The results showed that our PDA attention structure generated more discriminative change variance information while the entire network model obtained the best performance results with the same level of network model parameters in the transformer architecture. For LEVIR-CD, we achieved an intersection over union (IoU) of 0.8492 and an F1 score of 0.9185. For SYSU-CD, we obtained an IoU of 0.7028 and an F1 score of 0.8255. The experimental results showed that the method proposed in this paper was superior to some current state-of-the-art CD methods.
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