Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

Khaloo, Alireza; Lattanzi, David; Cunningham, Keith; Dell’Andrea, Rodney; Riley, Mark J.

doi:10.1080/15732479.2017.1330891

Cited by 166 publications

(81 citation statements)

References 31 publications

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“…Crack detection in the spatial domain using the Sobel filter resulted in the second highest number of TPs (43) and the second highest FP (8). Thus, while the Sobel filter was among the most accurate (85%), it was also among the least precise (84%).…”

Section: Spatial Domain Sobel Filtermentioning

confidence: 98%

“…Such inspections are costly, time-consuming, and labor-intensive [2,3]. Autonomous inspection could be a cost-effective solution to these problems if the accuracy of human inspection can be matched [3][4][5][6][7][8][9]. Image-based inspection of infrastructure for concrete delamination [10][11][12][13], cracks [14][15][16][17], and spalls [18,19] using unmanned aerial systems (UASs) have been proven effective based on previous literature [20].…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

2019

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This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

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Section: Spatial Domain Sobel Filtermentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

2019

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“…The image-based 3D model is also able to assist in condition assessment of in-service building and civil infrastructures. A recent application reconstructed an accurate model of a large-scale timber truss bridge using UAV and onboard camera (Khaloo et al 2017). The model generated based on a total of 22 flights with more than 2000 high resolution images.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time 3D Reconstruction on Construction Site Using Visual SLAM and UAV

Shang

Shen

2018

Construction Research Congress 2018

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3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the imagebased 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in Visual Simultaneous Localization and Mapping (SLAM) make it possible to reconstruct a 3D map of construction site in real-time. Integrated with Unmanned Aerial Vehicle (UAV), the obstacles areas that are inaccessible for the ground equipment can also be sensed. Despite these advantages of visual SLAM and UAV, until now, such technique has not been fully investigated on construction site. Therefore, the objective of this research is to present a pilot study of using visual SLAM and UAV for real-time construction site reconstruction. The system architecture and the experimental setup are introduced, and the preliminary results and the potential applications using Visual SLAM and UAV on construction site are discussed.

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“…To overcome these challenges, robot-assisted inspection apparatus coupled with various inspection techniques (Lee et al 2011(Lee et al , 2008Lim et al 2011;Mazumdar and Asada 2009;Murphy et al 2011;Oh et al 2007Oh et al , 2009Ward et al 2014), has been used for the bridge inspection. Recently, Unmanned Aerial Vehicles (UAVs) have been considered as a promising bridge inspection tool for the bridge inspection (Cunningham et al 2015;Otero et al 2015;Seo et al 2018b;c;Zink and Lovelace 2015;Hallermann and Morgenthal 2014;Khaloo et al 2018;Reagan et al 2017a;. A variety of researchers have demonstrated from different bridge inspection applications that UAV was a useful supplementary damage identification tool (Cunningham et al 2015;Duque et al 2018a;Otero et al 2015;Seo et al 2018a;c;Zink and Lovelace 2015).…”

Section: Introductionmentioning

confidence: 99%

New Bridge Inspection Approach with Joint UAV and DIC System

Jeong¹,

Seo²,

Wacker³

2020

Structures Congress 2020

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This research aims to develop a new bridge inspection approach using unmanned aerial vehicle (UAV) coupled with digital image correlation (DIC) system. The DIC system incorporating UAV images can measure displacements or strains by analyzing patterns of reference and deformed images. As part of this research, a commercially available UAV, DJI Matrice 210, was integrated with the DIC system using a 3D printed mounting plate, and the joint UAV-DIC system was utilized to inspect a timber bridge girder in the Structure Lab. Then, the UAV-DIC system inspected an existing timber slab bridge in Pipestone, Minnesota, but the system was not able to efficiently identify critical damage due to its instability caused by windy conditions. Therefore, only the UAV equipped with a gimbal camera was operated to perform the bridge inspection. A significant number of images from the UAV were used and analyzed through a conventional image analysis algorithm within ImageJ software for damage quantification. The major conclusion from this research was that the UAV-DIC system was only able to detect and quantify damage (i.e., crack) on the considered girder under almost zero ambient wind conditions, and the UAV integrated with the image analysis algorithm was capable of damage identification and quantification for the inspected bridge.

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Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

Cited by 166 publications

References 31 publications

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

Real-Time 3D Reconstruction on Construction Site Using Visual SLAM and UAV

New Bridge Inspection Approach with Joint UAV and DIC System

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