Semi-automatic calculation of joint trace length from digital images based on deep learning and data structuring techniques

Lee, Yong-ki; Kim, Jineon; Choi, Choongik; Song, Jae-Joon

doi:10.1016/j.ijrmms.2021.104981

Cited by 15 publications

(12 citation statements)

References 41 publications

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“…According to the detection indicator values, compared with UNet++ (Precision=0.853, Recall=0.872, F1-Score=0.862 and IoU=0.757), the network structure of UNet++(CBAM) is most suitable for microcrack detection (Precision=0.874, Recall=0.886, F1-Score=0.879 and IoU=0.785). Different from previous researches [1,15,24], the DeepLab V3+ and SegFormer did not show superior performance in this study, which is likely because of the types of datasets. The U-shaped network structure is more suitable for detecting subtle targets.…”

Section: Discussioncontrasting

confidence: 99%

“…The distribution of rock surface cracks is closely related to the mechanical and hydraulic properties of rock masses [1]. The quantitative characteristics of rock surface cracks are important for analyzing and assessing the stability of regional rock masses [2].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is necessary to optimize the network structure according to the specific application requirements [23]. The diversity of regional rock surface images and the irregularity of crack trajectories should be taken into account in the development of networks that can automatically detect crack pixels [1].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are few studies on how to extract and quantify crack parameters based on segmented images. In addition, in the study of information extraction of cracks, most researchers often skeletonize the detected crack traces to remove the thickness because the thickness of the detected traces is not meaningful in length calculation [1,3,11]. Although common image segmentation methods can detect crack traces with good connectivity, such methods significantly overestimate the thickness of detected traces.…”

Section: Introductionmentioning

confidence: 99%

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Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

Ye¹,

Yu²,

Zhang

et al. 2023

Preprint

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The process of image collection of high-altitude rock cracks using unmanned aerial vehicle (UAV) suffers from insufficient resolution and motion blur, which prevents more accurate detection of micro-cracks. Therefore, in this study, a rock crack refinement detection process (RC-RDP) based on super-resolution reconstruction (SRR) technique and semantic segmentation (SS) network is developed to detect micro-cracks. Four SRR networks (RCAN, SRDenseNet, ESRGAN, BSRGAN) and six SS networks (PSPNet, SegNet, DeepLab V3+, UNet++, UNet++(CBAM), SegFormer) are trained and tested separately using rock crack datasets. SRR indicators (PSNR and SSIM) and SS indicators (Precision, Recall, F1-Score and IoU) are applied to evaluate the performance of SRR networks and SS networks, respectively. According to the evaluation indicators of each network performance, in this paper, the RCAN network (PSNR = 31.08 dB and SSIM = 88.56%) is applied in the SRR part, and the UNet++ (CBAM) network is used in the crack detection part (Precision = 0.874, Recall = 0.886, F1-Score = 0.879 and IoU = 0.785). In addition, a novel data acquisition process integrating skeletonization algorithm, feature nodes detection algorithm, normal vector estimation algorithm and width extraction algorithm is used to calculate the minimum width WMin, maximum width WMax and average width WA information of the crack traces. The results of this paper show that the application of RC-RDP based on deep learning can obtain better super resolution (SR) images and more accurate width information. The method in this study shows excellent performance in detecting rock cracks.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

Ye¹,

Yu²,

Zhang

et al. 2023

Preprint

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“…The performance of CNN on complex surfaces such as rocks has not been sufficiently investigated yet. Lee et al (2022) applied the method with 57,024 images and the validation results of the model according to the Intersection over Union (IoU) metric was 0.611. The main reasons are the small size (thinness) of rock discontinuity delineations, higher dimensionality (3D) of the rock surface when compared with building façades and road surface, and the variations on the rock surface characteristics caused by colour reflectance and roughness, such as shadows.…”

Section: Photogrammetric Processing Resultsmentioning

confidence: 99%

A CNN Architecture for Discontinuity Determination of Rock Masses With Close Range Images

Yalcin

Can

Kocaman

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed.

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An adaptive methodology for rock mass fracture image enhancement with generalized gamma correction

Wu,

Sun

2023

Vis Comput

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Semi-automatic calculation of joint trace length from digital images based on deep learning and data structuring techniques

Cited by 15 publications

References 41 publications

Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

A CNN Architecture for Discontinuity Determination of Rock Masses With Close Range Images

An adaptive methodology for rock mass fracture image enhancement with generalized gamma correction

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