Conference Paper

Infrared and High-definition Image-based Bridge Scanning Using UAVs without Traffic Control

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... 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]. ...
... Edge images E(x, y) resulting from spatial or frequency domain edge detection filters contain a range of pixel intensities that require scaling. The scaling function given by Equation 13 converts edge image pixel intensities E(x, y) to linearly scaled edge image pixel intensities E (x, y) such that 0 I (x, y) 1. ...
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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.
... Almost 30 state DOTs have deployed sUAS for inspection or other purposes, either in practice or research [7]. The applications of sUAS can go far beyond an assistive tool for the inspector with the integration of image processing or machine learning techniques, which can be used for autonomous detection of cracks in concrete [10][11][12] or fatigue cracks in steel [13]. The feasibility and application of using deep convolutional neural networks for concrete crack detection in sUAS assisted inspection can provide a similar accuracy to human inspections [14]. ...
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Small unmanned aerial system(s) (sUAS) are rapidly emerging as a practical means of performing bridge inspections. Under the right condition, sUAS assisted inspections can be safer, faster, and less costly than manned inspections. Many Departments of Transportation in the United States are in the early stages of adopting this emerging technology. However, definitive guidelines for the selection of equipment for various types of bridge inspections or for the possible challenges during sUAS assisted inspections are absent. Given the large investments of time and capital associated with deploying a sUAS assisted bridge inspection program, a synthesis of authors experiences will be useful for technology transfer between academics and practitioners. In this paper, the authors list the challenges associated with sUAS assisted bridge inspection, discuss equipment and technology options suitable for mitigating these challenges, and present case studies for the application of sUAS to several specific bridge inspection scenarios. The authors provide information to sUAS designers and manufacturers who may be unaware of the specific challenges associated with sUAS assisted bridge inspection. As such, the information presented here may reveal the demands in the design of purpose-built sUAS inspection platforms.
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This study aims to reveal the effect and correlation of delamination size and defect shape for using infrared thermography (IRT) through FE modeling to enhance the reliability and applicability of IRT for effective structural inspections. Regarding the effect of delamination size, it is observed that the temperature difference between sound and delaminated area (\(\Delta \)T) increases as the size of delamination increases; however, \(\Delta \)T converges to a certain value when the area is 40 \(\times \) 40 cm and the thickness is 1 cm. As for the shape of delamination, it can be assumed that if the aspect ratio which is the ratio of the length of the shorter side to the longer side of the delamination is more than 25%, \(\Delta \)T of any delaminations converges to \(\Delta \)T of the same area of a square/circular-shaped delamination. Furthermore, if the aspect ratio is 25% or smaller, \(\Delta \)T becomes smaller than the \(\Delta \)T of the same area of a square/circular-shaped delamination, and it is getting smaller as the ratio becomes smaller. Furthermore, this study attempts to estimate depths of delaminations by using IRT data. Based on the correlation between the size of delamination and the depth from the concrete surface in regard to \(\Delta \)T, it was assumed that it was possible to estimate the depth of delamination by comparing \(\Delta \)T from IRT data to \(\Delta \)T at several depths obtained from FE model simulations. Through the investigation using IRT data from real bridge deck scanning, this study concluded that this estimation method worked properly to provide delamination depth information by incorporating IRT with FE modeling.
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Infrared and visible cameras were mounted on an unmanned aerial vehicle (UAV) to image bridge deck surfaces and map likely concrete delaminations. The infrared sensor was first tested on laboratory validation experiments, to assess how well it could detect and map delaminations under controlled conditions. Field tests on two bridge deck surfaces further extend the validation dataset to real-world conditions for heterogeneous concrete surfaces. Performance of the mapping instrument and algorithms were evaluated through receiver operating characteristic (ROC) curves, giving acceptable results. To improve the performance of the mapping by reducing the rate of false positives, i.e., areas wrongly mapped as being affected by delamination, visible images were jointly analyzed with the infrared imagery. The potential for expanding the method to include other products derived from the visible camera data, including high density 3D point locations generated by photogrammetric methods, promises to further improve the performance of the method, potentially making it a viable and more effective option compared to other platforms and systems for imaging bridge decks for mapping delaminations.
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There is a need for rapid and objective assessment of concrete bridge decks for maintenance decision making. Infrared Thermography (IRT) has great potential to identify deck delaminations more objectively than routine visual inspections or chain drag tests. In addition, it is possible to collect reliable data rapidly with appropriate IRT cameras attached to vehicles and the data are analyzed effectively. This research compares three infrared cameras with different specifications at different times and speeds for data collection, and explores several factors affecting the utilization of IRT in regards to subsurface damage detection in concrete structures, specifically when the IRT is utilized for high-speed bridge deck inspection at normal driving speeds. These results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. It is observed that nighttime would be the most suitable time frame with less false detections and interferences from the sunlight and less adverse effect due to direct sunlight, making more " noise " for the IRT results. This study also revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution.
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A detailed investigation of infrared thermography (IRT) for civil structures is presented by considering different technologies, data analysis methods and experimental conditions in the laboratory and also in the field. Three different types of infrared (IR) camera were compared under active IRT conditions in the laboratory to examine the effect of photography angle on IRT along with the specifications of cameras. It is found that when IR images are taken from a certain angle, each camera shows different temperature readings. However, since each IR camera can capture temperature differences between sound and delaminated areas, they have a potential to detect delaminated area under a given condition in spite of camera specifications even when they are utilized from a certain angle. Furthermore, a more objective data analysis method than just comparing IR images was explored to assess IR data, and it is much easier to detect delamination than raw IR images. Specially designed laboratory and field studies show the capabilities, opportunities and challenges of implementing IRT for civil structures.
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Capturing the temperature difference between sound and defective parts under ambient conditions is key for infrared thermography (IRT) on concrete bridges. This study explores the favorable time windows for concrete bridge deck inspections by IRT through field experiment and finite element model simulations. Based on the numerical simulations and experimental IRT results, the preferable thermal contrast to detect defects occurs during both daytime and nighttime. However, available time span during daytime is much shorter than that of nighttime due to interchange periods between cooling and heating cycles in the morning and in the evening. Furthermore, IRT is affected by sunlight during the daytime resulting in possible misdetections. Moreover, effects of clouds and radiative cooling are observed, and it is found that the clear sky is a preferable condition for IRT. Therefore, optimal conditions for IRT implementation on concrete bridge decks can be concluded that nighttime application under the clear sky condition. In addition, the effect of obstacles on a bridge surface such as gravel, wood chips that bring additional challenges to IRT are also evaluated experimentally.
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The main objective of this study is to comprehensively evaluate the utilization of infrared thermography (IRT) considering different technologies, critical environmental parameters, and uncertainties for bridge deck evaluation. For this purpose, a real bridge was scanned and the results were compared with other nondestructive evaluation (NDE) technologies that were implemented on the same bridge. There are a number of considerations and factors that affect the utilization of IRT, such as thermal contrasts, camera specifications, distance, and utilization speed, and these are evaluated by using three different infrared (IR) cameras with different specifications. These considerations are discussed and results are presented. When compared at fully documented locations with eight concrete cores, a high-end IR camera with cooled detector distinguished sound and delaminated areas accurately. Furthermore, indicated location and shape of delaminations by three IR cameras were compared with other NDE methods from past research, and the results revealed that the cooled camera showed shapes almost identical to other NDE methods including chain drag. It should be noted that these data were collected at a normal driving speed without any lane closures, making it a more practical and faster method than other NDE technologies. It is also presented that the factor most likely to affect high-speed application is integration time of the IR camera.
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The present study explores the potential application of unmanned aerial vehicle (UAV) Infrared Thermography for detecting subsurface delaminations in concrete bridge decks, which requires neither traffic interruption nor physical contact with the deck being inspected. A UAV-borne thermal imaging system was utilized to survey two in-service concrete bridge decks. The inspection process involved the acquisition of thermal images via low altitude flights using a high resolution thermal camera. The images were then enhanced and stitched together using custom developed codes to create a mosaic thermal image for the entire bridge deck. Image analysis based on the k-means clustering technique was utilized to segment the mosaic and identify objective thresholds. Hence, a condition map delineating different categories of delamination severity was created. The results were validated using data generated by other non-destructive testing technologies on the same bridge decks, namely hammer sounding and half-cell potential testing. The findings reveal that UAV with high-resolution thermal infrared imagery offers an efficient tool for precisely detecting subsurface anomalies in bridge decks. The proposed methodology allows more frequent and less costly bridge deck inspection without traffic interruption. This should enable rapid bridge condition assessment at various service live stages, thus effectively allocating maintenance and repair funds.
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This study presents a methodology to improve the usability and efficiency of infrared thermography (IRT) for subsurface damage detection in concrete structures. A practical and more objective approach to obtain a threshold for IRT data processing was developed by incorporating finite element (FE) model simulations. Regarding the temperature thresholds of sound and delaminated areas, the temperature of the sound part was obtained from the IR image, and the temperature of the delaminated area was defined by FE model simulation. With this methodology, delaminated areas of concrete slabs at 1.27 cm and 2.54 cm depths could be detected more objectively than by visually judging the color contrast of IR images. However, it was also found that the boundary condition affects the accuracy of the method, and the effect varies depending on the data collection time. On the other hand, it can be assumed that the influential area of the boundary condition is much smaller than the area of a bridge deck in real structures; thus, it might be ignorable on real concrete bridge decks. Even though there are some limitations, this methodology performed successfully paving the way towards automated IRT data analysis for concrete bridge deck inspections.
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Infrared thermography (IRT) has been used experimentally for concrete delamination detection. The past studies were conducted with limited experimental conditions, which make a difference in delamination detection. As a result, there are inconsistencies in the results reported in the literature. In this study, heat transfer models of concrete blocks with artificial delamination used for a previous test are developed and analyzed to explore sensitive parameters for effective utilization of IRT. After these FE models are validated, critical parameters and factors of delamination detectability such as the size of delamination (area, thickness and volume), ambient temperature and solar irradiance conditions (different seasons), and the depth of delamination from the surface are explored. This study presents that the area of delamination is much more influential in the detectability of IRT than thickness and volume. It is also found that there is no significant difference depending on the season when IRT is employed. This study shows a potential to bring significant improvement to IRT use in the field for subsurface damage detection for concrete structures.
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The rapid, cost-effective, and non-disruptive assessment of bridge deck condition has emerged as a critical challenge for bridge maintenance. Deck delaminations are a common form of deterioration which has been assessed, historically, through chain-drag techniques and more recently through nondestructive evaluation (NDE) including both acoustic and optical methods. Although NDE methods have proven to be capable to provide information related to the existence of delaminations in bridge decks, many of them are time-consuming, labor-intensive, expensive, while they further require significant disruptions to traffic. In this context, this article demonstrates the capability of unmanned aerial vehicles (UAVs) equipped with both color and infrared cameras to rapidly and effectively detect and estimate the size of regions where subsurface delaminations exist. To achieve this goal, a novel image post-processing algorithm was developed to use such multispectral imagery obtained by a UAV. To evaluate the capabilities of the presented approach, a bridge deck mockup with pre-manufactured defects was tested. The major advantages of the presented approach include its capability to rapidly identify locations where delaminations exist, as well as its potential to automate bridge-deck related damage detection procedures and further guide investigations using other higher accuracy and ground-based approaches.
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Infrared Thermography (IRT) is one of the nondestructive inspection techniques to detect delaminations in concrete bridge decks. These defects are identified by capturing the temperature gradient of concrete surfaces. In order for this technique to be effective in damage detection, IRT inspections should be conducted at certain time windows with favorable temperature conditions to get clear temperature gradients on inspected surfaces. This study is an experimental work examining the effects of ambient environmental conditions at different times of a day to locate subsurface delaminations and voids at a shallow depth, which is an additional influencing factor. This study also attempts to figure out a relationship between ambient environmental conditions and the temperature values of concrete surfaces to estimate the best time window with appropriate environmental conditions for IRT inspections. To this end, specially designed reusable concrete test plates with different thicknesses were manufactured to collect thermocouple sensor readings. Multiple regression analyses were employed to generate prediction models that seek a relationship between environmental conditions and temperature gradients on the test plates attached to a target bridge. Regression models also utilized sensor data collected at another location different than the target bridge location. It was found out that the most important aspect of sensor data collection was to accomplish a perfect contact of test plates with concrete bridge deck surfaces to get discernible temperature gradients. When this condition is not met, data analyses yield spurious results leading to futile conclusions. On the other hand, it was also observed that prediction models generated by regression analyses followed the same pattern as that of sensor readings. This makes it possible to have prediction equations based on sensor readings to determine suitable time window for conducting IRT inspections.
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Unmanned aerial vehicles (UAVs) allow remote imaging which can be useful in infrastructure condition evaluation. Furthermore, emerging noncontact sensing techniques such as digital imaging correlation (DIC) and other photogrammetric and visual approaches, including simultaneous localization and mapping (SLAM), can compute three-dimensional (3D) coordinates and perform deformation measurements as in the case of DIC/photogrammetry. A quantitative assessment of ways remote sensing in conjunction with UAVs could be implemented in practical applications is critically needed to leverage such capabilities in structural health monitoring (SHM). A comparative investigation of the remote sensing capabilities of a commercially availabl e UAV, as well as both an optical metrology system known by the acronym TRITOP and the X-Box Kinect, is presented in this paper. The evidence provided demonstrates that red-green-blue cameras on UAVs could detect, from varying distances, cracks of sizes comparable to those currently sought in visual inspections. In addition, mechanical tests were performed on representative bridge structural components to attempt, for the first time to the writers' best knowledge, deformation measurements using an aerial vehicle; displacements and corresponding accuracies were quantified in static and flying conditions. Finally, an outdoor feasibility test with the UAV was accomplished on a pedestrian bridge to test the marker identification algorithm.
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