Rapid detection of building cracks based on image processing technology with double square artificial marks

Yang, Gang; Wu, Jianchao; Hu, Qing

doi:10.1177/1369433218810183

Cited by 20 publications

(12 citation statements)

References 8 publications

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“…Using the same calculation method, /Gn1CFn by equation ( 14) was obtained at t n /Gn1CFn ¼ ACOS 1CGn 1CFn (14) In this study, the value of tilt angle change was obtained by equation (15).…”

Section: Methodsmentioning

confidence: 94%

“…The angle /G01CF0 in the initial calibration image was computed by equation (13). The angle /G01CF0 in the nth image was computed by equation (14). The /GnHG0 value of building tilt angle change at nth observation was realized by equation (15).…”

Section: Building Tilt Angle Change Calculation and Real-time Monitoringmentioning

confidence: 99%

“…13 The double square marks method was well established for building crack width detection. 14 This paper proposed a novel test method for building tilt angle monitoring based on double square marks.…”

Section: Introductionmentioning

confidence: 99%

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Real-time monitoring of changes in building tilt angle using double square artificial marks

Yang

et al. 2020

Indoor and Built Environment

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To monitor changes in building tilt angles more accurately and efficiently and to safeguard the people's safety and property, a new tilt angle monitoring method was proposed in this paper. This method adopts double square artificial marks and a plumb line determined by MATLAB to record changes in the building tilt angle based on image processing technology. Two black square artificial marks with the size of 10 mm × 10 mm were placed near the side of the plumb line. A digital camera was adopted to collect tilt images which were saved in the computer. The tilt images were successively subjected to image greying, binarization, denoising, image segmentation and pixel calibration. Finally, the true length of a single pixel and tilt angle change value would be obtained. The experimental results showed that the accuracy of the method was 96% higher than the manual method. Compared with traditional manual methods and three-dimensional scanning monitoring methods, the advantages of this method are that the equipment used is relatively cheap, the image calculation workload is small and is suitable in dangerous working conditions. In conclusion, the proposed technique could accurately, concisely and effectively calculate the changes in building tilt angle and improve work efficiency.

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Section: Methodsmentioning

confidence: 94%

Section: Building Tilt Angle Change Calculation and Real-time Monitoringmentioning

confidence: 99%

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Real-time monitoring of changes in building tilt angle using double square artificial marks

Yang

et al. 2020

Indoor and Built Environment

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“…Their method is restricted to constant illumination and angle at which the image recording system is set towards the recorded surface. Yang et al [17] used an image processing technique called the double square artificial mark method to detect cracks in buildings and estimate their width. Although, it was demonstrated that their method is highly accurate in crack width estimation, it needs two black square artificial marks to be placed near the sides of the crack, which can be a restriction in some cases.…”

Section: Introductionmentioning

confidence: 99%

A new image processing strategy for surface crack identification in building structures under non‐uniform illumination

Parrany

Mehrara

2021

IET Image Processing

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Crack detection in building structures is an important approach to evaluating the safety of these structures. However, in some cases under special circumstances, it is very difficult to identify all features of cracks by visual inspection. To tackle this problem and offer a higher potential for practical implementations, developing an automatic crack detection method seems essential. This paper establishes a novel algorithm for surface crack detection in building walls based on image processing techniques to enhance crack visibility. One of the most prevalent issues in surface crack detection from digital crack images is non-uniform illumination of background which makes narrow and tiny cracks indistinguishable. In this regard, an integrated approach, combining adaptive image threshold using local first-order statistics, contrast-limited adaptive histogram equalization, and noise filtering using nonlinear diffusion filtering, is proposed to extract the whole skeleton of the crack from digital crack images with non-uniform illumination. Several realistic crack images, suffering from noise and uneven lighting of background, are processed through the proposed algorithm to demonstrate the robustness and high accuracy of the method.

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“…Recently, SHM based on computer vision has attracted extensive attention in civil engineering, such as visual inspection and damage detection (Gao and Mosalam, 2018;Morgenthal and Hallermann, 2014), crack width measurement (Coca et al, 2020;Yang et al, 2019), and dynamic displacement measurement (Dong and Catbas, 2019;Dong et al, 2020). The vehicle re-identification based on computer vision in the large-scale traffic monitoring scene can be regarded as a problem of the nearest neighbor search for repetitive image retrieval (Mei et al, 2014), is of substantial significance in intelligent traffic (Zhang et al, 2011), urban computing (Zheng et al, 2014), and road travel time estimation (Kwong et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A deep neural network-based vehicle re-identification method for bridge load monitoring

Zhang

Xie

Peng

et al. 2021

Advances in Structural Engineering

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The accurate tracking of vehicle loads is essential for the condition assessment of bridge structures. In recent years, a computer vision method that is based on multiple sources of data from monitoring cameras and weight-in-motion (WIM) systems has become a promising strategy in bridge vehicle load identification for structural health monitoring (SHM) and has attracted increasing attention. The implementation of vehicle re-identification, namely, the identification of the same vehicle from images that were captured at different locations or time instants, is the key topic of this study. In this study, a vehicle re-identification method that is based on HardNet, a deep convolutional neural network (CNN) specialized in picking up local image features, is proposed. First, we obtain the vehicle point feature positions in the image through feature detection. Then, the HardNet is employed to encode the point feature image patches into deep learning feature descriptors. Re-identification of the target vehicle is achieved by matching the encoded descriptors between two images, which are robust toward scaling, rotation, and other types of noises. A comparison study of the proposed method with three published vehicle re-identification methods is performed using vehicle image data from a real bridge, and the superior performance of our proposed method is demonstrated.

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Rapid detection of building cracks based on image processing technology with double square artificial marks

Cited by 20 publications

References 8 publications

Real-time monitoring of changes in building tilt angle using double square artificial marks

Real-time monitoring of changes in building tilt angle using double square artificial marks

A new image processing strategy for surface crack identification in building structures under non‐uniform illumination

A deep neural network-based vehicle re-identification method for bridge load monitoring

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