KEYWORDS: Calibration, Cameras, 3D image processing, Sensors, Sensor calibration, Digital image correlation, 3D metrology, Imaging systems, Prototyping, Structural health monitoring
Stereovision systems can extract full-field three-dimensional (3D) displacements of structures by processing the images collected with two synchronized cameras. To obtain accurate measurements, the cameras must be calibrated to account for lens distortion (i.e., intrinsic parameters) and compute the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking photos of a calibration object (e.g., a checkerboard) with the two cameras. Because the calibration object must be similar in size to the targeted structure, measurements on large-scale structures are highly impractical. This research proposes a multi-sensor board with three inertial measurement units and a laser distance meter to compute the extrinsic parameters of a stereovision system and streamline the calibration procedure. In this paper, the performances of the proposed sensor-based calibration are compared with the accuracy of the traditional image-based calibration procedure. Laboratory experiments show that cameras calibrated with the multi-sensor board measure displacements with 95% accuracy compared to displacements obtained from cameras calibrated with the traditional procedure. The results of this study indicate that the sensor-based approach can increase the applicability of 3D digital image correlation measurements to large-scale structures while reducing the time and complexity of the calibration.
KEYWORDS: Cameras, Calibration, Sensors, Gyroscopes, Sensor calibration, Imaging systems, Digital image correlation, 3D image processing, Structural health monitoring, Monte Carlo methods
Three-dimensional digital image correlation (3D-DIC) has become a strong alternative to traditional contact-based techniques for structural health monitoring. 3D-DIC can extract the full-field displacement of a structure from a set of synchronized stereo images. Before performing 3D-DIC, a complex calibration process must be completed to obtain the stereovision system’s extrinsic parameters (i.e., cameras’ distance and orientation). The time required for the calibration depends on the dimensions of the targeted structure. For example, for large-scale structures, the calibration may take several hours. Furthermore, every time the cameras’ position changes, a new calibration is required to recalculate the extrinsic parameters. The approach proposed in this research allows determining the 3D-DIC extrinsic parameters using the data measured with commercially available sensors. The system utilizes three Inertial Measurement Units with a laser distance meter to compute the relative orientation and distance between the cameras. In this paper, an evaluation of the sensitivity of the newly developed sensor suite is provided by assessing the errors in the measurement of the extrinsic parameters. Analytical simulations performed on a 7.5 x 5.7 m field of view using the data retrieved from the sensors show that the proposed approach provides an accuracy of ~10-6 m and a promising way to reduce the complexity of 3D-DIC calibration.
KEYWORDS: Bridges, Cameras, Structural health monitoring, Video, Data acquisition, Video processing, Reliability, Machine learning, Shape analysis, Algorithm development
Applications of motion magnification has been seen as an effective way to extract pertinent structural health monitoring data without the use of instrumentation. In particular, phase-based motion magnification (PMM) has been adopted to amplify subtle motions that cannot be seen clearly without further processing. For large infrastructure, this tool can be helpful in identifying the dynamic range of motion and modal frequencies. The use of accelerometers poses a problem for structures that contain large geometry, due to the complexities that arise when attempting to setup a modal test. Optically, one can identify singular points or regions of interest that capture a large range of motion for a structure. These regions of interest ultimately provide the dynamic information that is needed to perform structural health monitoring (SHM) of a complex system. This paper aims to identify a shift in frequency and operational deflection shapes due to varying loading scenarios while using PMM. The ability to capture multiple points without being limited by a data acquisition system permits further analysis of structural health. For example, the ability to apply varying loading scenarios can provide warnings as to how a frequency shifts while sustaining a particular force. Due to the plethora of loading conditions, the variation in external loading makes SHM a more conclusive process. For instance, it was applied many different scenarios for loading conditions and damages to observe the shifts in the frequencies due to each factor. It was also done testing with different sensing techniques and with traditional sensing to verify the reliability of PMM. The tests were done in laboratory structures and in real structures to prove the applicability of PMM and to verify what information is needed to identify damage in the structure.
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