Fatigue Crack Detection Using Unmanned Aerial Systems in Fracture Critical Inspection of Steel Bridges

Dorafshan, Sattar; Thomas, Robert J.; Maguire, Marc

doi:10.1061/(asce)be.1943-5592.0001291

Cited by 72 publications

(106 citation statements)

References 16 publications

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“…Crack detection in the frequency domain using the Butterworth filter resulted in the median number of TPs (40) and the median FP (7). The accuracy (83%) and precision (85%) were also near the median of the six methods evaluated.…”

Section: Frequency Domain Butterworth Filtermentioning

confidence: 80%

“…Figure 6 shows representative enhanced edge, first-level binary, and second-level binary images from spatial domain edge detection of an image from the defected set using the Sobel filter. Crack detection in the spatial domain using the Prewitt filter resulted in the second lowest number of TP (41) and the highest FP (9). The Prewitt filter was the second least accurate and the least precise of the six methods evaluated.…”

Section: Spatial Domain Sobel Filtermentioning

confidence: 92%

“…This gave the edge detectors an advantage over human inspectors. 4 False Negative Rate, 5 Accuracy, 6 Precision, and 7 Missed Crack Width…”

Section: Spatial Domain Roberts Filtermentioning

confidence: 99%

“…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: Frequency Domain Butterworth Filtermentioning

confidence: 80%

Section: Spatial Domain Sobel Filtermentioning

confidence: 92%

“…This gave the edge detectors an advantage over human inspectors. 4 False Negative Rate, 5 Accuracy, 6 Precision, and 7 Missed Crack Width…”

Section: Spatial Domain Roberts Filtermentioning

confidence: 99%

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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“…e assessment outcomes of human inspectors are largely determined by their level of training and experience. Notably, the observation of inspectors is also hindered by irregular lighting conditions and elevations [7]. ese facts significantly reduce the accuracy of the manual crack detection.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

Hoang

Nguyen

2018

Advances in Civil Engineering

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Crack detection is a crucial task in the periodic survey of high-rise buildings and infrastructure. Manual survey is notorious for low productivity. is study is aimed at establishing an image processing-based method for detecting cracks on concrete wall surfaces in an automatic manner. e Roberts, Prewitt, Canny, and Sobel algorithms are employed as the edge detection methods for revealing the crack textures appearing in concrete walls. e median filtering and object cleaning operations are used to enhance the image and facilitate the crack recognition outcome. Since the edge detectors, the median filter, and the object cleaning operation all require the appropriate selection of tuning parameters, this study relies on the differential flower pollination algorithm as a metaheuristic to optimize the image processing-based crack detection model. Experimental results point out that the newly constructed approach that employs the Prewitt algorithm can achieve a good prediction outcome with classification accuracy rate � 89.95% and area under the curve � 0.90. erefore, the proposed metaheuristic optimized image processing approach can be a promising alternative for automatic recognition of cracks on the concrete wall surface.

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Probability of detection curve for the automatic visual inspection of steel bridges

Kompanets,

Leonetti,

Duits

et al. 2023

ce papers

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The damage tolerant design philosophy is based on periodical inspections and provides safe bridge operation preventing fatigue cracks from growing up to a critical size. It is possible to optimize the management costs of a bridge throughout its life by designing the inspection frequency, which depends on the capabilities of the inspection method. However, such optimization requires knowledge about the performance of inspections that are envisioned to take place during the use of the bridge. Regular visual inspections is the most frequently applied type of inspection of bridge structures. Recent advances in computer vision technologies provide a strong basis for the development of automatic damage detection systems that can support regular visual inspection, thus increasing the reliability of the inspection. Several automatic crack detection systems have been developed in the past years. However, the performances of such systems have not been evaluated in the way as for traditional non‐destructive inspection methods, i.e. in terms of probability of detection curves and detectability limits. This restricts the applicability of automatic visual inspections for inspection planning and damage tolerant design. This paper proposes an encoder‐decoder neural network for segmentation of cracks on images of steel bridges. A probability of detection curve is calculated for this neural network.

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Fatigue Crack Detection Using Unmanned Aerial Systems in Fracture Critical Inspection of Steel Bridges

Cited by 72 publications

References 16 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

Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

Probability of detection curve for the automatic visual inspection of steel bridges

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