Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition

Hadavandsiri, Zahra; Lichti, Derek D.; Jahraus, Adam; Jarron, David

doi:10.3390/ijgi8120585

Cited by 13 publications

(11 citation statements)

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“…A more sensitive detector provides more useful information to detecting wall surface changes. Many studies have shown that radiometric information of the point cloud can be effectively used to damage detection of buildings and structures [31,66].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Time-of-Flight and Phase-Shift TLS Intensity Data for the Diagnostics Measurements of Buildings

Suchocki

2020

Materials

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In recent years, the terrestrial laser scanning system (TLS) has become one of the most popular remote and nondestructive testing (NDT) methods for diagnostic measurements of buildings and structures as well as for the assessment of architectural heritage. Apart from 3D coordinates, the power of a laser beam backscattered from the scanned object can be captured by TLS. The radiometric information of the point cloud, called “intensity”, can provide information about changes in the physio–chemical properties of the scanned surface. This intensity can be effectively used to detect defects in the surfaces of walls, such as cracks and cavities, moisture, biodeterioration (mosses and lichens) or weathered parts of the wall. Manufacturers of TLS mainly use two different principles for distance measurement, time-of-flight (TOF) and phase-shift (PS). The power of energy in both types of rangefinders might be absorbed or reflected in a slightly different way and provide more or less detailed radiometric point cloud information. The main aim of this investigation is to compare TOF and PS scanners in the context of using TLS intensity data for the diagnostics of buildings and other structures. The potential of TLS intensity data for detecting defects in building walls has been tested on multiple samples by two TOF (Riegl VZ400i, Leica ScanStation C10) and two PS (Z + F 5016 IMAGER, Faro Focus3D) scanners.

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Section: Discussionmentioning

confidence: 99%

Comparison of Time-of-Flight and Phase-Shift TLS Intensity Data for the Diagnostics Measurements of Buildings

Suchocki

2020

Materials

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“…for profile and areal features), a set of points is queried from within a defined 3D search radius of the pixel of interest. This optimal radius size has to be small enough to allow detection of the finest surface details, however, big enough to average out the effects of noise present in the datasets (Hadavandsiri et al 2019). The obtained subset of points is then used to determine the feature vector of that single point, and this procedure is repeated for all the points within each dataset.…”

Section: Surface Quality Assessmentmentioning

confidence: 99%

“…Moreover, Kazemian et al (2019) explored not only the nozzle distance from the surface but also the width of the layer that is extruded, thus adapting the process in a way to reach the desired outcome. Off-line concrete damage inspection using TLS and involving surface 3D feature computation is presented in Hadavandsiri et al (2019), and inspection using TLS intensity image classification in Zaczek-Peplinska and Osińska-Skotak (2018). According to the authors knowledge, the potential of geometric feedback systems is not yet fully exploited and further developments are needed, as will be presented in this contribution.…”

Section: Introductionmentioning

confidence: 99%

Geometric Feedback System for Robotic Spraying

Frangež¹,

Taha²,

Feihl³

et al. 2022

PFG

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In this paper, we tackle the task of replacing labor intensive and repetitive manual inspection of sprayed concrete elements with a sensor-based and automated alternative. We present a geometric feedback system that is integrated within a robotic setup and includes a set of depth cameras used for acquiring data on sprayed concrete structures, during and after fabrication. The acquired data are analyzed in terms of thickness and surface quality, with both sets of information then used within the adaptive fabrication process. The thickness evaluation is based on the comparison of the as-built state to a previous as-built state or to the design model. The surface quality evaluation is based on the local analysis of 3D geometric and intensity features. These features are used by a random forest classifier trained using data manually labelled by a skilled professional. With this approach, we are able to achieve a prediction accuracy of 87 % or better when distinguishing different surface quality types on flat specimens, and 75 % when applied in a full production setting with wet and non-planar surfaces. The presented approach is a contribution towards in-line material thickness and surface quality inspection within digital fabrication.

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“…There are two main methods for extracting cracks from 3D point clouds. (1) projecting dense 3D point clouds into 2D images and extracting cracks using image-processing techniques [ 21 , 22 ]; and (2) extracting cracks directly from the 3D point clouds [ 23 , 24 ]. Based on the first method, Yang et al [ 25 ] used the reflectance information from point clouds to detect cracks in a tunnel structure through morphology and a Canny edge detection operator, but there were many fractures in the detected cracks.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Chen

Huang

et al. 2021

Sensors

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Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

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Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition

Cited by 13 publications

References 50 publications

Comparison of Time-of-Flight and Phase-Shift TLS Intensity Data for the Diagnostics Measurements of Buildings

Comparison of Time-of-Flight and Phase-Shift TLS Intensity Data for the Diagnostics Measurements of Buildings

Geometric Feedback System for Robotic Spraying

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

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