Photogrammetric methods such as structure from motion (SFM) have the capability to acquire accurate information about geometric features, surface cracks, and mechanical properties of specimens and structures in civil engineering. Conventional approaches to verify the accuracy in photogrammetric models usually require the use of other optical techniques such as LiDAR. In this paper, geometric accuracy of photogrammetric modeling is investigated by studying the effects of number of photos, radius of curvature, and point cloud density (PCD) on estimated lengths, areas, volumes, and different stress states of concrete cylinders and panels. Four plain concrete cylinders and two plain mortar panels were used for the study. A commercially available mobile phone camera was used in collecting all photographs. Agisoft PhotoScan software was applied in photogrammetric modeling of all concrete specimens. From our results, it was found that the increase of number of photos does not necessarily improve the geometric accuracy of point cloud models (PCM). It was also found that the effect of radius of curvature is not significant when compared with the ones of number of photos and PCD. A PCD threshold of 15.7194 pts/cm3 is proposed to construct reliable and accurate PCM for condition assessment. At this PCD threshold, all errors for estimating lengths, areas, and volumes were less than 5%. Finally, from the study of mechanical property of a plain concrete cylinder, we have found that the increase of stress level inside the concrete cylinder can be captured by the increase of radial strain in its PCM.

Accuracy analysis of point cloud modeling for evaluating concrete specimens

Determining the geographic location of an object using two-dimensional (2D) images recorded at high-oblique angles is a nontrivial problem. Existing methods to solve this problem rely on parameters that are either difficult to measure or are based on assumptions. This paper investigates the accuracy of building photogrammetric models using machine learning. Our novel approach involves the collection of training examples before using supervised learning to build a nonlinear, multitarget prediction model. We collected training examples using an unmanned ground vehicle (UGV) that moved throughout the fields of view of multiple cameras. The UGV was tracked and bounded using existing computer vision techniques. With each image frame, the center pixel position (x, y image coordinates) of the vehicle and its bounding box area (in pixels) were mapped to its current GPS coordinates. Multiple machine learning models were created using various combinations of cameras to determine the key features for building accurate photogrammetric models. Data was collected under realistic conditions for ground-based surveillance systems, which may require cameras to be placed at low elevations and high-oblique angles. We found the prediction accuracy of our models to be between 0.58 and 3.54 meters depending upon a number of factors, including the locations, heights, and orientations of the cameras used.

A Machine Learning Framework for Building Passive Surveillance Photogrammetry Models

Photogrammetry is a task-oriented and multi-stage measurement metrology and its accuracy is affected by many factors, which is the most important and difficult point in this field A new accuracy evaluation method for photogrammetry system based on 3D reference field is proposed in this paper, and the effect of optical axes angles and base lines is studied The 3D reference technology for this accuracy evaluation method which can provide the conversion between laser tracking system and photogrammetry is designed and built The stability experiments of the reference field are carried out and the results show that the repeatability of point measurement reaches 1 μm and satisfies the requirement According to the accuracy evaluation method and experiments based on the 3D reference field, the relationships between the system error and optical axes angles, base lines are established, which will guide the design and implementation of measurement schemes

Accuracy evaluation method and experiments for photogrammetry based on 3D reference field

Videogrammetry is a task-oriented and multi-stage metrology and its precision assessment which is influenced by many factors is the important and difficult point of research. In order to analyze and assess the configurations and stages, the simulation system for the optimization of videogrammetry is designed, which can provide network configurations, precision assessment, etc.. Moreover, the precision assessment of images is the basis of the 3D reconstruction point error estimation. A new method to assess the precision of images is proposed in this paper and the 2D standard conference plate is designed. In this new method, the influences of all factors are taken into account together and the interrelations can be ignored. So the precision assessment of images is simplified. With the experimental results, the feasibility of this method has been testified by the application of the simulation system in the measurement of gossamer inflatable antenna.

Research on the Precision Assessment of Images for Videogrammetry

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Accuracy evaluation method and experiments for photogrammetry based on 3D reference field

Research on the Precision Assessment of Images for Videogrammetry