A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications

Abstract

In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision-based technology have been exploited in the field of structural health monitoring (SHM). This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large-range and multiple-target monitoring. A lot of machine vision-based structural dynamic measurement and structural state inspection methods have been proposed. Real-world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision-based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.

Determination of Geometric Parameters of Cracks in Concrete by Image Processing

“…e 2 nd setup consists of a binocular microscope (5) for magnifying the crack image, a Raspberry Pi camera v2 (3) installed at one eye position of the microscope for recording the image, and the halogen lamp (4), the same as in the 1 st setup. ese optical setups are on an optical table (9), and the Raspberry Pi camera is connected to a monitor (8) to check the crack image. e Nikon camera has the maximum resolution of 7360 × 4912 pixels with a pixel size of 4.88 µm × 4.88 µm [32].…”

“…e changing sizes and depths of the cracks in time are a barometer of predicting the safety of a structure. Hence, the demands of measuring the geometrical parameters of the cracks accurately are ever increasing [4][5][6][7][8], and the number of articles for crack measurements has also been increased [9][10][11][12][13][14][15][16][17]. In this process, many methods such as using ultrasounds [18][19][20][21], X-rays [22], and eddy current (EC) [23] sources are developed, and the images are reconstructed in 3D (three-dimensional) [24] form.…”

A Novel Dense Full-Field Displacement Monitoring Method Based on Image Sequences and Optical Flow Algorithm

“…With the continuous development of machine vision technology and image acquisition equipment, structural displacement monitoring methods based on computer vision continues to emerge and have been verified in practical engineering applications [26][27][28][29][30]. Given its long-distance, noncontact, high-precision, time-saving, labor-saving, multi-point detection, and many other advantages, as well as increasing attention has been received from scientific researchers and engineers [31]. This method is mainly used to track the target of the measured structure video, which is captured by the camera, to obtain the moving track of the measuring point in the image, and then determine the displacement information of the structure through the set relationship between the image and the real world.…”

Review of machine-vision based methodologies for displacement measurement in civil structures

“…This paper aims to present a summary of key work in the field of vision-based systems for structural displacement monitoring while highlighting the principles, advantages and shortcomings of these systems. Although previous reviews of vision-based structural monitoring exist [27][28][29], the contribution of this work is to provide an overview of system classifications, methodologies and applications in field monitoring.…”