Wire mesh filtering in 3D image data of rock faces

Mah, Jason; McKinnon, Stephen D.; Samson, C.; Thibodeau, Denis

doi:10.1016/j.tust.2015.11.005

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Contact methods include profilograph (Du et al, 2009) and profile gauge (Barton and Choubey, 1977). Non-contact methods can be listed as shadow profilometer (Franklin, et al, 1988), laser imaging (Mah et al, 2013(Mah et al, , 2016, photogrammetry (Jessell et al 1995;Cravero et al 2001; Lee and Ahn 2004;Unal et al 2004;Bistacchi et al 2011;Sturzenegger and Stead 2009;Kim et al, 2015;Vollgger and Cruden 2016), an X-ray computed tomography (Diaz et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Joint roughness profiling using photogrammetry

Saricam

Ozturk

2022

Appl Geomat

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We propose an automated camera setup for photogrammetric roughness analysis in the laboratory environment. The developed fast and low-cost automation setup can be very useful for tedious and laborsome manual field logging practices. The photographs are processed in MATLAB to obtain disparity maps. Coding routines for stereo photogrammetry and digital measurements are written in MATLAB. Secondly, 6 effecting factors (projecting an image onto core face, depth of field, brightness, camera-to-object to baseline distance ratio, projected image size and occlusion) influencing noise in roughness depth maps computed by employing stereo photogrammetry are investigated. After deciding the best values that allow the lowest amount of noise, depth maps of 6 core faces are computed. Using the 3D point cloud generated, roughness profile measurements are made. Then, 8 profile measurements are made for each core face, both manually and digitally.The accuracy of the disparity maps has been verified by comparing 48 joint roughness coefficient (JRC) measurements made manually using a profile gauge. It was proved that surface roughness can be measured very fast in millimetric accuracy with an average Root Mean Square Error (RMSE) of 3.50 and Mean Absolute Error (MAE) of 3.02 by the help of the proposed set-up and calibration.

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Section: Introductionmentioning

confidence: 99%

Joint roughness profiling using photogrammetry

Saricam

Ozturk

2022

Appl Geomat

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“…The study of rock drilling fields is a more popular research area in tunnel construction, and is not needed the detection of arches. References [8], [9] introduce a method to detect an arch by manual division and Mah et al [10] proposed a Principal Component Analysis (PCA)-based method to detect the mesh in point clouds. However, these methods are not sufficiently automated or precise.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR

et al. 2020

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Shotcrete spray is an indispensable process in tunnel construction. At present, the construction of tunnels in China is mainly depend on labor or mobile concrete sprayer, which has lots problems like timeconsuming, low precision, and labor intensive. An intelligent detection method for tunnel shotcrete spraying is proposed in this article. There are two main issues need to be solved, one is the modeling of tunnel in real-time to monitor the thickness of shotcrete and other is the detection of spraying area in the tunnel. The LiDAR can obtain a 3D model of tunnel after performing necessary preprocess on it in real-time. On the other hand, the spraying areas are usually divided by arches in the tunnel, so we can detect the position of arches to determine the spraying areas. Inspired by the YOLO algorithm, we proposed a novel neural network structure to detect the approximate bounding boxes of the arches and a line-detection algorithm is used to determine the final positions of the spraying area in the image. The size of the weight file of our neural network is only 2.57 MB after the use of some deep compression tricks, which means our model is device friendly. After that, the object detection results in the image will be projected to the point cloud data. The experimental results suggest that our method performed well in the detection for tunnel shotcrete spraying, and the mAP for spraying area detection was found to be 91.4%. INDEX TERMS 3D point cloud, deep learning, object detection, tunnel shotcrete.

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“…Cheng et al [23] designed an angle-based morphology-related filtering method, which automatically and indiscriminately removing all non-lining points from non-circular tunnel sections. Mah et al [24] explored the removal of steel mesh in tunnel surface data, and adopted the Principal Component Analysis (PCA) method, analyzing the local area of the network without considering the shape type of the whole tunnel. These previous studies on tunnel cross-section detection usually adopted a semi-automatic method, which requires manual adjustment and modification of algorithm parameters in the processing process.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

Zhang

Qiu

Song

et al. 2019

Sensors

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Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region-growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F-score.

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Wire mesh filtering in 3D image data of rock faces

Cited by 8 publications

References 25 publications

Joint roughness profiling using photogrammetry

Joint roughness profiling using photogrammetry

Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

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