Abstract
Information such as cracks and deflections is the important basis for structural safety. Existing methods have not achieved simultaneous detection. In most existing computer vision measurement systems, the view is fixed due to the fixed position of the camera. Thus, it is difficult to obtain the structures’ overall crack and deflection information. An automatic response measurement method is proposed in this study including ( 1 ) continuous image acquisition and signal transmission system based on self-walking bracket and Internet of Things (IoT), ( 2 ) an image splicing method based on feature matching, and ( 3 ) a crack and deflection measurement method. The self-walking bracket allows the industrial camera to move at a fixed distance to obtain the continuous image of the beam. Next, the spliced image is obtained through the PCA-SIFT method with a screening mechanism. The cracks’ information is acquired by the dual network model. The simplified AKAZE feature detection algorithm and the modified RANSAC are used to track the natural features of the structures. The curve fitting is performed to obtain the deflection curve of the beam under different loads. Experimental results show that the method can directly reflect the crack and deflection information of the beam. The average deviation of width is 11.76%, average deviation of length is 8.18%, and the average deformation deviation is 4.50%, which verifies the practicability of the method and shows great potential to apply it in actual structures.
Highlights
Structures and infrastructure systems including bridges, buildings, dams, and pipelines are complex engineering systems that support a society’s economic prosperity and quality of life
Over the past two decades, a significant amount of studies have been conducted in the emerging field of structural health monitoring, aiming at objective and quantitative structural damage detection and integrity assessment based on measurements by sensors, mostly accelerometers [1,2,3,4,5,6,7,8,9,10,11,12]. eir wide deployment in realistic engineering structures is limited by cumbersome and expensive installation, maintenance of sensors networks, and data acquisition (DAQ) systems
Crack identification based on computer vision mainly includes threshold segmentation, edge detection, and penetration models. e convolutional neural networks (CNNs) [13, 14] based on deep learning have achieved fruitful results, which have been widely used in image recognition
Summary
Structures and infrastructure systems including bridges, buildings, dams, and pipelines are complex engineering systems that support a society’s economic prosperity and quality of life. 2. The Overall Framework of the Proposed System e automatic measurement method proposed in this study can be divided into three parts: continuous image acquisition and signal transmission system, image splicing system, crack identification and deflection measurement system. The framework of the proposed method mainly contains three steps: the first is to construct a continuous image acquisition and signal transmission system based on a self-walking bracket and IoT. E last step is to send the spliced images to the crack identification system and the deflection measurement system to detect the response of the beam. Is method ensures that the industrial camera keeps a safe distance from the structure and moves along the beam direction to obtain continuous images. E operation steps of the continuous image acquisition and signal transmission system are as follows: Step 1: place the self-walking bracket parallel to the beam. Step 2: control the working status of the bracket and camera by computer. e process can monitor the detection status in real time. e camera takes
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