Can We Measure Displacement of a Structure with Featureless Surface using Computer Vision?
- Non-target based computer vision methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation.
- This paper presents a reference frame-based Deepflow algorithm integrated with masking and signal filtering for non-target-based displacement measurements.
- The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error.
- The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions.
How can we minimize power consumption of IoT sensor while capturing unexpected events such as earthquake, failure, etc.
- Despite the advantages of the Wireless sensor networks (WSNs) for large infrastructure monitoring, long-term structural health monitoring, however, is still a challenge because it requires continuous data acquisition for the detection of random events such as earthquakes and structural collapse.
- To achieve long-term operation, it is necessary to reduce the power consumption of sensor nodes designed to capture random events and, thus, enhance structural safety.
- In this paper, we present an event-based sensing system design based on an ultra-low-power microcontroller with programmable event-detection mechanism to allow continuous monitoring; the device is triggered by vibration, strain, or a timer and has a programmed threshold, resulting in ultra-low-power consumption of the sensor node.
