PTZ Camera-Based Displacement Sensor System with Perspective Distortion Correction Unit for Early Detection of Building Destruction

This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls the zoom level of the lens for a constant field of view (FOV). The proposed approach adopts perspective distortion correction to expand the measurable range in monitoring the displacement of the target structure. The implemented system successfully obtains the displacement information in structures, which is not easily accessible on the remote site. We manually measured the displacement acquired from markers which is attached on a sample of structures covering a wide geographic region. Our approach using a PTZ-based camera reduces the perspective distortion, so that the improved system could overcome limitations of previous works related to displacement measurement. Evaluation results show that a PTZ-based displacement sensor system with the proposed distortion correction unit is possibly a cost effective and easy-to-install solution for commercialization

Long-term displacement measurement of full-scale bridges using computer vision and lidar

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Bridge displacement is regarded as a key safety indicator that is widely adopted for structural health monitoring (SHM). Bridge structures deflect in response to applied loads and structural degradation. As extensive vibrations of bridges cause passenger???s discomfort and accelerate structural degradation, modern societies take the bridge displacement into account in their design codes and regular maintenance protocols to ensure serviceability and safety of the bridge structures. The short-term displacement is generally employed in bridge SHM, together with the level of load carrying capacity. Even though the long-term displacement can also provide essential safety information, in addition to the short-term data, the long-term displacement monitoring of bridges is not commonly conducted owing to practical difficulties. The long-term monitoring of displacement using conventional displacement sensors, such as a linear variable differential transformer, laser displacement sensor, and radar, or indirect estimation methods, such as an acceleration-based method or multimetric sensor-based approaches result in errors, which typically accumulate over time. A limited number of research studies have addressed long-term bridge displacement measurementhowever, the sensor drift can still cause errors in those measurements. This paper proposes long-term displacement measurement methods using computer vision and LiDAR, tailored to full-scale bridge structures. The computer vision-based approach compensates for the camera motion-induced errors by using an auxiliary camera and the long-term displacement can be achieved regardless of the camera movement. A LiDAR-based method is also presented, by which the long-term time history of the bridge displacement can be tracked by a temporarily installed LiDAR, thus eliminating the need for a permanent installation in the field. These two long-term measurement approaches were cross validated on a 40 m-long full-scale railway bridge under construction. Over a span of 650 days, these two methods showed a similar trend, thus validating the applicability of each method. Important structural information, such as immediate displacement due to dead load, long-term deflection due to creep, daily fluctuation due to temperature gradient, could potentially provide long-term displacement data in bridge health monitoring.clos