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

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 dierent 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

Understanding Surprise: Can Less Likely Events Be Less Surprising?

Surprise is often thought of as an experience that is elicited following an unexpected event. However, it may also be the case that surprise stems from an event that is simply difficult to explain. In this paper, we investigate the latter view. Specifically, we question why the provision of an enabling factor can mitigate perceived surprise for an unexpected event despite lowering the overall probability of that event. One possibility is that surprise occurs when a person cannot rationalise an outcome event in the context of the scenario representation. A second possibility is that people can generate plausible explanations for unexpected events but that surprise is experienced when those explanations are uncertain. We explored these hypotheses in an experiment where a first group of participants rated surprise for a number of scenario outcomes and a second group rated surprise after generating a plausible explanation for those outcomes. Finally, a third group of participants rated surprise for the both the original outcomes and the reasons generated for those outcomes by the second group. Our results suggest that people can come up with plausible explanations for unexpected events but that surprise results when these explanations are uncertain

Understanding Surprise: Can Less Likely Events Be Less Surprising?

Surprise is often thought of as an experience that is elicited following an unexpected event. However, it may also be the case that surprise stems from an event that is simply difficult to explain. In this paper, we investigate the latter view. Specifically, we question why the provision of an enabling factor can mitigate perceived surprise for an unexpected event despite lowering the overall probability of that event. One possibility is that surprise occurs when a person cannot rationalise an outcome event in the context of the scenario representation. A second possibility is that people can generate plausible explanations for unexpected events but that surprise is experienced when those explanations are uncertain. We explored these hypotheses in an experiment where a first group of participants rated surprise for a number of scenario outcomes and a second group rated surprise after generating a plausible explanation for those outcomes. Finally, a third group of participants rated surprise for the both the original outcomes and the reasons generated for those outcomes by the second group. Our results suggest that people can come up with plausible explanations for unexpected events but that surprise results when these explanations are uncertain

School-based management : conditions for implementation.

This study in the field of School-Based Management was initiated to determine under what conditions a School-Based Management/Shared Decision Making (SBM/SDM) Organization might be considered a viable alternative to a traditional (top-down) school structure. The primary focus of this study sought to discover the extent to which school principals and teachers can and should participate in making educational decisions within an urban school system. To ascertain this information, open-ended structured interviews were developed and conducted with five randomly selected principals. In addition, a close-ended questionnaire was designed and distributed to teachers. In order to ensure broad-based definitive conclusions, participants represented all four school zones and each educational level. Methodological limitations include a survey of two hundred and sixteen school-based teachers with a response rate of fifty-three percent. Findings identify six key elements to be considered when implementing a SBM/SDM structure. The six key elements are money, trust, training, accountability, participative decision making, and union involvement. Recommendations involving the implementation of School-Based Management are offered in addition to suggested future studies. Results of this study indicate that both principals and teachers have a strong desire to actively participate in budget, personnel, and curriculum decisions effecting their schools

First record of biofluorescence in lumpfish (Cyclopterus lumpus), a commercially farmed cleaner fish

This study is the first known observation of biofluorescence in the lumpfish (Cyclopterus lumpus). Individual lumpfish were illuminated with blue excitation lighting for photography with both hyperspectral and filtered multispectral cameras. All photographed juvenile lumpfish (n = 11) exhibited green biofluorescence. Light emissions were characterised with two peaks observed at 545 and 613 nm, with the greatest intensity along the tubercles of the high crest and the three longitudinal ridges. Further research on the dynamics of biofluorescence through the lifecycle of this species is required

Area deprivation and the food environment over time: A repeated cross-sectional study on takeaway outlet density and supermarket presence in Norfolk, UK, 1990-2008.

Socioeconomic disparities in the food environment are known to exist but with little understanding of change over time. This study investigated the density of takeaway food outlets and presence of supermarkets in Norfolk, UK between 1990 and 2008. Data on food retail outlet locations were collected from telephone directories and aggregated within electoral wards. Supermarket presence was not associated with area deprivation over time. Takeaway food outlet density increased overall, and was significantly higher in more deprived areas at all time points; furthermore, socioeconomic disparities in takeaway food outlet density increased across the study period. These findings add to existing evidence and help assess the need for environmental interventions to reduce disparities in the prevalence of unhealthy food outlets.Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, (ES/G007462/1), and the Wellcome Trust, (087636/Z/08/Z), under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.This is the final published version. It first appeared at www.sciencedirect.com/science/article/pii/S1353829215000325

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

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 dierent 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

Comparison of Deep Convolutional Neural Networks and Edge Detectors for Image-Based Crack Detection in Concrete

This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they produced residual noise in the final binary images. The best of these methods was useful in detecting cracks wider than 0.1 mm. DCNNs were used to label images, and accurately labeled them with 99% accuracy. In transfer learning mode, the network accurately detected about 86% of cracked images. DCNNs also detected much finer cracks than edge detection methods. In fully trained and classifier modes, the network detected cracks wider than 0.08 mm; in transfer learning mode, the network was able to detect cracks wider than 0.04 mm. Computational times for DCNN are shorter than the most efficient edge detection algorithms, not considering the training process. These results show significant promise for future adoption of DCNN methods for image-based damage detection in concrete. To reduce the residual noise, a hybrid method was proposed by combining the DCNN and edge detectors which reduced the noise by a factor of 24

SDNET2018: An Annotated Image Dataset for Non-Contact Concrete Crack Detection Using Deep Convolutional Neural Networks

SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field of structural health monitoring. The authors present benchmark results for crack detection using SDNET2018 and a crack detection algorithm based on the AlexNet DCNN architecture. SDNET2018 is freely available at https://doi.org/10.15142/T3TD19

Setting and motivation in the decision to participate: An approach to the engagement of diverse samples in mobile research.

Internet and mobile based research are powerful tools in the creation of large, cohort studies (eCohort). However, recent analysis indicates that an underrepresentation of minority and low income groups in these studies might exceed that found in traditional research [1-5]. In this report, we present findings from an experiment in research engagement using the Eureka Research Platform developed to enroll diverse populations in support of biomedical clinical research. This experiment involved the recruitment of African American and Latino participants in a smartphone based survey at a temporary, charitable, dental event sponsored, in part, by the research team, in order to explore the impact of setting and approach on recruitment outcomes. 211 participants enrolled including a significant representation of African Americans (51%) and Latinos (31%) and those with education levels at high school or less (37%). Interviews conducted after the study confirmed that our recruitment efforts within the context of a service event affected the decision to participate. While further research is necessary, this experiment holds promise for the engagement of underrepresented groups in research