Deformation Detection of Mining Tunnel Based on Automatic Target Recognition

Mining tunnels have irregular and diverse cross-sectional shapes. Structural deformation detection using mobile laser measurement has some problems, such as the inconvenient positioning of the deformation, difficulties in unifying the multiphase data, and difficulties in solving the section parameters. To address these problems, this paper proposes a mining tunnel deformation detection method based on automatic target recognition. Firstly, a mobile tunnel laser detection scheme combined with the target layout is designed. Secondly, a preview image of the tunnel lining is generated using the mobile laser point cloud data, and the index relationship between the image and point cloud is established. The target recognition accuracy of the You Only Look Once version 4 (YOLOv4) model is optimized by integrating the prediction confidence threshold, target spatial position, and target gray scale rule. Based on target recognition and positioning, the chord length and vault net height of the mining tunnel are calculated using gross error elimination and curve fitting. Finally, the engineering application of the model and algorithm is realized using ML.NET. The research method was verified using the field measurement data of the mining tunnel. The target recognition accuracy reached 100%, and the repeated deviations of the chord length and net height of the arch crown were 1.7 mm and 1.4 mm, respectively, which established the effectiveness and high accuracy of the research method

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

Mobile laser scanning technology used for deformation detection of shield tunnel is usually two-dimensional, which is expanded into three-dimensional (3D) through mileage, resulting in low positioning accuracy. This study proposes a 3D laser point cloud positioning method that is divided into rings in the mileage direction and blocks in the ring direction to improve the positional accuracy for shield tunnels. First, the cylindrical tunnel wall is expanded into a plane and the bolt holes are extracted using the self-adaptive parameter adjustment cloth simulation filter (CSF) algorithm combined with a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Second, the mean-shift algorithm is used to obtain the center point of the bolt hole, and a model is designed to recognize the center point of different splicing blocks. Finally, the center point is combined with the standard straight-line equation to fit the straight-line positioning seam, achieving an accurate ring and block segmentation of a shield tunnel as a 3D laser point cloud. The proposed method is compared with existing methods to verify its feasibility and high accuracy using the seams located by the measured tunnel point cloud data and in the measured point cloud. The average differences between the circumferential seams positioned using the proposed method and those in the point cloud at the left waist, vault, and right waist were 3, 4, and 5 mm, respectively, and the average difference between the longitudinal seams was 3.4 mm The proposed research method provides important technical and theoretical support for tunnel safety monitoring and detection

Deformation Detection of Mining Tunnel Based on Automatic Target Recognition

Mining tunnels have irregular and diverse cross-sectional shapes. Structural deformation detection using mobile laser measurement has some problems, such as the inconvenient positioning of the deformation, difficulties in unifying the multiphase data, and difficulties in solving the section parameters. To address these problems, this paper proposes a mining tunnel deformation detection method based on automatic target recognition. Firstly, a mobile tunnel laser detection scheme combined with the target layout is designed. Secondly, a preview image of the tunnel lining is generated using the mobile laser point cloud data, and the index relationship between the image and point cloud is established. The target recognition accuracy of the You Only Look Once version 4 (YOLOv4) model is optimized by integrating the prediction confidence threshold, target spatial position, and target gray scale rule. Based on target recognition and positioning, the chord length and vault net height of the mining tunnel are calculated using gross error elimination and curve fitting. Finally, the engineering application of the model and algorithm is realized using ML.NET. The research method was verified using the field measurement data of the mining tunnel. The target recognition accuracy reached 100%, and the repeated deviations of the chord length and net height of the arch crown were 1.7 mm and 1.4 mm, respectively, which established the effectiveness and high accuracy of the research method

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

Mobile laser scanning technology used for deformation detection of shield tunnel is usually two-dimensional, which is expanded into three-dimensional (3D) through mileage, resulting in low positioning accuracy. This study proposes a 3D laser point cloud positioning method that is divided into rings in the mileage direction and blocks in the ring direction to improve the positional accuracy for shield tunnels. First, the cylindrical tunnel wall is expanded into a plane and the bolt holes are extracted using the self-adaptive parameter adjustment cloth simulation filter (CSF) algorithm combined with a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Second, the mean-shift algorithm is used to obtain the center point of the bolt hole, and a model is designed to recognize the center point of different splicing blocks. Finally, the center point is combined with the standard straight-line equation to fit the straight-line positioning seam, achieving an accurate ring and block segmentation of a shield tunnel as a 3D laser point cloud. The proposed method is compared with existing methods to verify its feasibility and high accuracy using the seams located by the measured tunnel point cloud data and in the measured point cloud. The average differences between the circumferential seams positioned using the proposed method and those in the point cloud at the left waist, vault, and right waist were 3, 4, and 5 mm, respectively, and the average difference between the longitudinal seams was 3.4 mm The proposed research method provides important technical and theoretical support for tunnel safety monitoring and detection

Enhanced Water Leakage Detection in Shield Tunnels Based on Laser Scanning Intensity Images Using RDES-Net

Tunnel water leakage detection is the difficulty and bottleneck in shield tunnel operation monitoring. This article introduces the RDES method for detecting water leakage in shield tunnels. First, to enhance the model's information extraction capabilities, we introduce a detection-efficient efficient 3 normalized conv1ds of attention module by combining the characteristics of the attention mechanisms, efficient channel attention, and coordinate attention. This module aims to focus the model's backbone network on capturing the spatial features of water leakage data and facilitating meaningful interaction between different channels. In addition, we design the residual deformable convolution to provide the convolutional energy with the flexibility to detect deformed targets, thereby enhancing the model's recognition ability. Finally, we incorporate a nonmaximum suppression technique during prediction to improve detection accuracy. This technique retains more bounding boxes by applying soft-NMS weighted averaging. The results of the measured and publicly available data show that the RDES algorithm outperforms all the compared algorithms and is advanced and efficient. The results show that compared with the baseline, the RDES algorithm improves the F1 metrics, mAP0.5, mAP0.75, and mAP by 2.2%, 4.4%, 9.2%, and 5.2% on the laser intensity image dataset, and by 3.1%, 4.6%, 7.9%, and 5.9% on the optical image dataset

3D Point Cloud Generation Based on Multi-Sensor Fusion

Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels

3D Point Cloud Generation Based on Multi-Sensor Fusion

Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels

GL-Net: semantic segmentation for point clouds of shield tunnel via global feature learning and local feature discriminative aggregation

Has gradually become the first choice of modern urban public transportation due to its advantages of safety and high-efficiency. Shield tunnel is an important type of subway tunnel, and its structural stability and safety play an important role in subway operation. The shield tunnels are prone to problems such as water leakage and tunnel collapse, which affect the safe operation of subways. Efficient monitoring methods are required to detect the status of subway tunnels. The data collection and accurate segmentation of key components of shield tunnels are the basis and key to the automatic monitoring of subway tunnels. This research presents a novel semantic segmentation method of three-dimensional (3-D) point clouds of typical structural elements (e.g., longitudinal joint, circumferential joints, bolt hole and grouting hole) in shield tunnel based on deep learning. In this method, we focus on how to make the network learn robust global features and complex local distribution patterns. Further, we propose a global and local feature encoding block (namely GL-block) to discriminatively aggregate local features while learning global representation. After multiple encodings by the GL-block, we design a global correlation modeling (GCM) module to establish a global awareness of each point. Finally, a weighted cross-entropy loss function is designed to solve the problem of unbalanced number of samples in each category of shield tunnel. In the experiments, we make a dataset of shield tunnel point clouds with a length of about 1,000 m collected by CNU-TS-1 (DU et al., 2018) mobile tunnel monitoring system, and use the dataset to train and test the segmentation ability of our method on the typical structural elements of shield tunnels. Experiments verify the effectiveness of our method by comparing with the other state-of-the-art 3-D point cloud semantic segmentation methods, and our method has an mIoU score of 73.02 %, which is at least 14.54 % higher than the other compared state-of-the-art networks. Also, we further verify the adaptability of our method to different tunnels and different laser scanning equipment, such as FARO, Leica and Z + F, and achieve very advanced performance