Challenges in bridge inspection using small unmanned aerial systems: Results and lessons learned

Dorafshan, Sattar; Maguire, Marc; Hoffer, Nathan V.; Coopmans, Calvin

doi:10.1109/icuas.2017.7991459

Cited by 48 publications

(41 citation statements)

References 14 publications

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“…• FAA mandates the pilot has a line-of-sight to the vehicle during the inspection. However, one of the advantages of using UASs is to access to locations that are difficult to reach without a UBIT [46,117,118].…”

Section: Faa Restriction To Uas Bridge Inspectionmentioning

confidence: 99%

“…Part of this will come with future automation of the inspection process, but currently, image-processing techniques for damage detection and 3D modeling are not at the level required for even a semiautomated real-time inspection. Whether for image modifications like removing image distortion or for intelligent feature detection algorithms like image-based crack detection, the post-processing operations have been commonly used for UAS bridge inspection research, but are still not time or cost effective for most bridges at this time [100,117]. Performing these complex operations is costly and requires professional and highly trained staff, which are inaccessible to most DOTs [110].…”

Section: Timementioning

confidence: 99%

“…Inspections are often scheduled many months out without the possibility of returning due to tight DOT and private inspector schedules, although inspection dates can become more flexible when a UBIT is not involved. The quality of the UAS flight and the acquired data can decrease due to adverse weather [102,117]; furthermore, captured images or videos may not be clear due to the variable lighting conditions underneath a bridge. High wind speeds will significantly increase the allowable clearance between the UASs and the object of interest because of the risk for damaging sensitive mechanical equipment or even the structure itself [110,118].…”

Section: Weathermentioning

confidence: 99%

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Bridge inspection: human performance, unmanned aerial systems and automation

Dorafshan

Maguire

2018

J Civil Struct Health Monit

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123

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Unmanned Aerial Systems (UASs) have become of considerable private and commercial interest for a variety of jobs and entertainment in the past 10 years. This paper is a literature review of the state of practice for the United States bridge inspection programs and outlines how automated and unmanned bridge inspections can be made suitable for present and future needs. At its best, current technology limits UAS use to an assistive tool for the inspector to perform a bridge inspection faster, safer, and without traffic closure. The major challenges for UASs are satisfying restrictive Federal Aviation Administration regulations, control issues in a GPS denied environment, pilot expenses and availability, time and cost allocated to tuning, maintenance, post-processing time, and acceptance of the collected data by bridge owners. Using UASs with self-navigation abilities and improving imageprocessing algorithms to provide results near real-time could revolutionize the bridge inspection industry by providing accurate, multi-use, autonomous three-dimensional models and damage identification.

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Section: Faa Restriction To Uas Bridge Inspectionmentioning

confidence: 99%

Section: Timementioning

confidence: 99%

Section: Weathermentioning

confidence: 99%

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Bridge inspection: human performance, unmanned aerial systems and automation

Dorafshan

Maguire

2018

J Civil Struct Health Monit

Self Cite

123

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“…The most common inspection type is routine inspection, wherein the inspector scans the bridge deck to identify surface degradation or surface cracking. 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].…”

Section: Introductionmentioning

confidence: 99%

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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“…In recent years, there has been increasing efforts in the development of field robots for performing tasks that are dangerous, tiring, tedious, and/or difficult for humans to perform. Such tasks, not limited to disaster response (Kojima et al, ; Y. Zhang et al, ), also include bridge inspection (Dorafshan, Maguire, Hoffer, & Coopmans, ), powerline inspection (L. Zhang & He, ), dam inspection (Shimono, Toyama, & Nishizawa, ), and agricultural harvesting (Yaguchi, Nagahama, Hasegawa, & Inaba, ).…”

Section: Introductionmentioning

confidence: 99%

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

Chan¹,

Mizohana²,

Chen³

et al. 2018

Journal of Field Robotics

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The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.

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Challenges in bridge inspection using small unmanned aerial systems: Results and lessons learned

Cited by 48 publications

References 14 publications

Bridge inspection: human performance, unmanned aerial systems and automation

Bridge inspection: human performance, unmanned aerial systems and automation

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

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

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