Quantification of water inflow in rock tunnel faces via convolutional neural network approach

Chen, Jiayao; Zhou, Mingliang; Zhang, Dongming; Huang, Hongwei; Zhang, Feng-Shou

doi:10.1016/j.autcon.2020.103526

Cited by 48 publications

(31 citation statements)

References 53 publications

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“…In recent years, photogrammetry has developed rapidly as a non-destructive inspection method for tunnel structure health monitoring [33][34][35]65]. By mounting high-resolution linear charge coupled device (CCD) cameras onto a movable inspection platform, photogrammetry can achieve continuous scanning imaging of the tunnel lining surface along the longitudinal direction.…”

Section: D Inspection Results Of a Testing Tunnel Sectionmentioning

confidence: 99%

“…have been proposed for detecting target objects to meet the needs of each specialized application. For inspection tasks during tunnel operation, scholars have conducted many studies into the automated detection of concrete spalling [3,26], lining cracks [27][28][29], water leakages [30][31][32], and rock mass evaluation [33][34][35] based on the DCNN models. Relying on the colour feature differences, the DCNN-based models show superior performance in distinguishing target objects from the context of the images.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

Zhou

Cheng

Huang

et al. 2021

Sensors

Self Cite

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The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility.

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Section: D Inspection Results Of a Testing Tunnel Sectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

Zhou

Cheng

Huang

et al. 2021

Sensors

Self Cite

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show abstract

“…Commonly, deep learning models require huge data to train where transfer learning strategy is utilized. This paper employs three pre-trained neural network architectures, namely ResNet18, ResNet50, and ResNet101 [35][36][37][38][39]. In 2016, ResNet (short for Residual Network) was a specific type of neural network previously introduced by He et al [35].…”

Section: Deep Learningmentioning

confidence: 99%

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

Alquran¹,

Al-Issa²,

Alslatie³

et al. 2023

Computer Systems Science and Engineering

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Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images. This paper proposed two models; the first one employed the 3D features while the second exploited the 2D features. The first system uses 3D texture attributes, 3D shape features, and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories. On top of that, the proposed method is applied to 2D slices for comparison purposes. The proposed approach attained 100% accuracy in discriminating between all types of tumors, 100% Area Under the Curve (AUC), 100% sensitivity, and 100% specificity and precision as well in 3D liver tumors. On the other hand, the performance is lower in 2D classification. The maximum accuracy reached 96.4% for two classes and 92.1% for four classes. The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes. The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input, besides comparing the performance between 2D and 3D classification. In the future, the presented work can be extended to be used

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“…In order to effectively use the optical flow map and RGB image for depth estimation, we need to combine them. The concept of our proposed method is based on the atrous convolution in [ 33 ], and we exploit a heat map from the luminance values of the optical flow map and embed it into the RGB image. The heat-map is embedded at a certain number of intervals such that the features in the original RGB image is not lost.…”

Section: A Pix2pix-based Monocular Depth Estimation With Optical Flowmentioning

confidence: 99%

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

Shimada

Nishikawa

Kong

et al. 2022

Sensors

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In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix generates depth images from a monocular camera. Additionally, this work applies optical flow to enhance the accuracy of depth estimation. In this work, we propose a highly accurate depth estimation method that effectively embeds an optical flow map into a monocular image. The models are trained with taking advantage of AirSim, which is one of the flight simulators. AirSim can take both monocular and depth images over a hundred meter in the virtual environment, and our model generates a depth image that provides the long-distance information than images captured by a common depth camera. We evaluate accuracy and error of our proposed method using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance. As a result, our proposed method is higher accuracy and lower error than a state of work. Moreover, our proposed method is lower collision than a state of work.

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Quantification of water inflow in rock tunnel faces via convolutional neural network approach

Cited by 48 publications

References 53 publications

A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

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