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.

https://www.mdpi.com/1996-1944/13/2/353/pdf

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

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.

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

/pdf/geometric-modelling-and-calibration-of-a-spherical-camera-2yjtr23vuv.pdf

Geometric modelling and calibration of a spherical camera imaging system

Detection of non-overlapping ellipses from 2-dimensional (2D) edge points is an essential step towards solving typical photogrammetry problems pertaining to feature detection, calibration, and registration of optical instruments. For instance, circular and spherical black and white calibration and registration targets are represented as ellipses in images. Furthermore, the intersection of a cut plane with cylindrical point clouds generates 2D points following elliptic patterns. To this end, this study proposes a collection of new methods for the automatic and robust detection of non-overlapping ellipses from 2D points. These methods will first be applied to detect circular and spherical targets in images and, second, to detect cylinders in 3D point clouds. The method utilizes the Euclidian ellipticity and a new systematic and generalizable threshold to decide if a set of connected points follow an elliptic pattern. When connected points include outliers, the newly proposed robust Monte Carlo-based ellipse fitting method will be deployed. This method includes three new developments: (i) selecting initial subsamples using a bucketing strategy based on the polar angle of the points; (ii) detecting inlier points by reducing the robust ellipse fitting to a robust circle fitting problem; and (iii) choosing the best inlier set amongst all subsamples using adaptive, systematic, and generalizable selection criteria. A new process is presented to extract cylinders from a point cloud by detecting non-overlapping ellipses from the points projected onto an intersecting cut plane. The proposed methods were compared to established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. The experiments include (i) comparisons of robust ellipse fitting; (ii) sensitivity analysis of the ellipse validation criteria; (iii) comparison of non-overlapping ellipse detection; and (iv) detection of pipes from terrestrial laser scanner point clouds. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3% on real images, compared to 42.4%, 65.6%, and 59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively. The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds collected in laboratory and industrial construction site conditions. The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.

Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds

3D point clouds have emerged as a crucial source of information in the field of Cultural Heritage due to their ability to capture reality with high density and accuracy. They are commonly used as inputs for generating planimetries, Building Information Models or numerical simulations. However, their potential goes beyond these applications and can be further exploited, particularly in the realm of detecting and quantifying damage. Taking this into consideration, the aim of this study is to conduct a systematic review to compile previous experiences in which the 3D point cloud has been applied for the detection of damage in historical constructions. A total of 188 articles were analysed during this review, emphasizing the capability of this technology in detecting damage in historical constructions. The review revealed a growing trend in this approach, particularly in the structural field where the terrestrial laser scanner is the most commonly used sensor. This is due to the capacity of capturing reality with great density and accuracy, allowing for the extraction of different type of damages related to the geometry of the elements such as deflections or out-of-plane deformations. However, this is not the only valuable information. 3D point clouds, which can also be obtained through the Structure From Motion approach, are capable of storing radiometric information in the form of intensity and/or digital levels. This information can be utilized for detecting other types of damage, such as moisture or crusts, among others. All this information is properly synthetized and presented, in accordance with different aspects such as the general properties of the 3D point clouds, the use of geometric and radiometric features, the integration of additional sensors, and their use in other approaches. Additionally, the work proposes a novel classification system for damage detection based on 3D point clouds. This system is based on the type of feature/s used. 

Detection of damage in heritage constructions based on 3D point clouds. A systematic review

This paper presents a novel approach for automatic, preliminary detection of damage in concrete structures using ground-based terrestrial laser scanners. The method is based on computation of defect-sensitive features such as the surface curvature, since the surface roughness changes strongly if an area is affected by damage. A robust version of principal component analysis (PCA) classification is proposed to distinguish between structural damage and outliers present in the laser scanning data. Numerical simulations were conducted to develop a systematic point-wise defect classifier that automatically diagnoses the location of superficial damage on the investigated region. The method provides a complete picture of the surface health of concrete structures. It has been tested on two real datasets: a concrete heritage aqueduct in Brooks, Alberta, Canada; and a civil pedestrian concrete structure. The experiment results demonstrate the validity and accuracy of the proposed systematic framework for detecting and localizing areas of damage as small as 1 cm or less.

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

. Wide-angle lenses typically offer fields of view greater than 70°, which are utilized in a variety of imaging, mapping, and navigation applications. Wide-angle lenses are commonly modelled using the central perspective model, compensating for lens distortions through a series of additional parameters. The more extreme the distortions, the further the reality of the lens matches the collinearity equations that define the central perspective model. Fisheye lenses are modelled differently because their fields of view are so wide (typically 180°) that the collinearity model is not applicable. This work studied the effects of modelling wide-angle lenses using both the conventional central perspective model and the fisheye model to determine which model best fits the observations and models the distortions more precisely and accurately. These results were produced by generating observations in a dedicated indoor calibration facility at the University of Calgary: an 11 m × 11 m × 4 m field comprising 291 signalized photogrammetry targets. Multiple free-network, self-calibrating bundle adjustments were performed using different models and different cameras. The results of the self-calibrating bundle adjustments were then utilized in a check adjustment on independent sets of check images to validate their accuracy. Two cameras, a Ladybug5 and a GoPro Hero5, were tested. The GoPro was also calibrated using a checkerboard target pattern, and the results were compared to those of the 3D calibration target-field. The results of the bundle adjustments determined that the fisheye model describes the distortions more precisely in both wide-angle camera systems.

/pdf/modelling-wide-angle-lens-cameras-for-metrology-and-mapping-2ui8oglc2q.pdf

Modelling wide-angle lens cameras for metrology and mapping applications

. In this work, a new method is developed for the automatic and accurate detection and labelling of signalized, un-coded circular targets for the purpose of automated camera calibration in a test field. The only requirements of this method are the approximate height of the camera, an approximate range of orientations of the camera, and the object-space coordinates of the targets. In each image, circular targets are detected using adaptive thresholding and robust ellipse fitting. Labelling of those targets is performed next. First, the exterior orientation parameters of the image are estimated using a one-point pose-estimation approach, where a list of possible orientation and target labels are used, along with height, to calculate the camera position. The estimated position and orientation of the camera combined with the interior orientation parameters (IOPs) are then used to back-project the known object-space coordinates of the targets into the image space. These targets are then matched against the targets detected in the image, and the list entry with the best fit is chosen as the solution. This resolves both the detection and labelling of the targets, without the need for any coded targets or their associated software packages, and each image is solved independently allowing for parallel processing. This process accurately labels 92–97% of images, with average accuracy rates of 97% or better, and average completeness rates of 70–95% in imagery from the three cameras tested. The cameras were calibrated using observations from the detection and labelling process, which resulted in sub-pixel root mean square (RMS) values determined for the pixel space residuals.

/pdf/automatic-detection-and-labelling-of-photogrammetric-control-2nwal99ak1.pdf

Automatic detection and labelling of photogrammetric control points in a calibration test field

Multi-camera panoramic imaging system calibration

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