Image processing algorithms for crack detection in welded structures via pulsed eddy current thermal imaging

ABSTRACTRapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on image-processing algorithms and machine learning analysis. We captured thermal images of the back of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in detecting COVID-19 with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. We show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

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The Use of Features from Fluorescence, Thermography, and NDVI Imaging to Detect Biotic Stress in Lettuce

Plant Disease ◽

10.1094/pdis-10-17-1536-re ◽

2018 ◽

Vol 102 (6) ◽

pp. 1101-1107 ◽

Cited By ~ 8

Author(s):

Martin Sandmann ◽

Rita Grosch ◽

Jan Graefe

Keyword(s):

Image Processing ◽

Biotic Stress ◽

Thermal Imaging ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Maximum Quantum Yield ◽

Whole Plant ◽

Image Processing Algorithms ◽

Processing Algorithms ◽

Horticultural Practice

Fluorescence, normalized difference vegetation index, and thermal imaging are three frequently used nondestructive methods to detect biotic stress in plants. Due, in part, to the inconsistent results reported in the literature and the lack of measurements on the whole-plant scale, we tested the suitability of a wide variety of variables obtained using these three imaging methods to classify young plants into biotically stressed and nonstressed plants. To this end, we applied the model plant–pathogen system lettuce–Rhizoctonia solani. The relevant data from each image and plant (healthy and diseased) was extracted semiautomatically using sophisticated image processing algorithms. This method enabled us to identify the most appropriate variables via discriminant function and logistic regression analysis: photosystem II maximum quantum yield (Fv/Fm) and fluorescence decline ratio can be used to classify variables with an error ≤0.052. Lettuce seedlings with an Fv/Fm ratio > 0.73 were consistently healthy. In some cases, it was possible to detect infection prior to the appearance of symptoms. Possibilities to transfer the method to horticultural practice are discussed.

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Automated processing of thermal imaging to detect COVID-19

Scientific Reports ◽

10.1038/s41598-021-96900-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rafael Y. Brzezinski ◽

Neta Rabin ◽

Nir Lewis ◽

Racheli Peled ◽

Ariel Kerpel ◽

...

Keyword(s):

Image Processing ◽

Low Income ◽

Thermal Imaging ◽

Point Of Care ◽

Area Under The Curve ◽

Screening Tools ◽

Imaging Device ◽

Thermal Images ◽

Image Processing Algorithms ◽

Processing Algorithms

AbstractRapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on advanced image processing algorithms. We captured thermal images of the backs of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in COVID-19 detection with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. In summary, we show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

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

Infrastructures ◽

10.3390/infrastructures4020019 ◽

2019 ◽

Vol 4 (2) ◽

pp. 19 ◽

Cited By ~ 13

Author(s):

Dorafshan ◽

Thomas ◽

Maguire

Keyword(s):

Image Processing ◽

Edge Detection ◽

Frequency Domain ◽

Crack Width ◽

Crack Detection ◽

Spatial Domain ◽

Gaussian Filter ◽

Laplacian Of Gaussian Filter ◽

Image Processing Algorithms ◽

Processing Algorithms

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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Otoconia perfect/imperfect crystals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100167949 ◽

1994 ◽

Vol 52 ◽

pp. 42-43

Author(s):

César D. Fermin ◽

Dale Martin

Keyword(s):

Image Processing ◽

Room Temperature ◽

Phosphotungstic Acid ◽

Gallus Domesticus ◽

Crystal Matrix ◽

Organic Templates ◽

Image Processing Algorithms ◽

Processing Algorithms ◽

Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

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Analysis of field uniformity and quantitative evaluation of subsurface pitting corrosion in conductors via GPEC

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209303 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 19-29

Author(s):

Shuting Ren ◽

Yong Li ◽

Bei Yan ◽

Jinhua Hu ◽

Ilham Mukriz Zainal Abidin ◽

...

Keyword(s):

Quantitative Evaluation ◽

Eddy Current ◽

Pitting Corrosion ◽

Gradient Field ◽

Uniform Field ◽

Eddy Current Method ◽

Optimal Position ◽

Pulsed Eddy Current ◽

Field Uniformity ◽

Processing Algorithms

Structures of nonmagnetic materials are broadly used in engineering fields such as aerospace, energy, etc. Due to corrosive and hostile environments, they are vulnerable to the Subsurface Pitting Corrosion (SPC) leading to structural failure. Therefore, it is imperative to conduct periodical inspection and comprehensive evaluation of SPC using reliable nondestructive evaluation techniques. Extended from the conventional Pulsed eddy current method (PEC), Gradient-field Pulsed Eddy Current technique (GPEC) has been proposed and found to be advantageous over PEC in terms of enhanced inspection sensitivity and accuracy in evaluation and imaging of subsurface defects in nonmagnetic conductors. In this paper two GPEC probes for uniform field excitation are intensively analyzed and compared. Their capabilities in SPC evaluation and imaging are explored through simulations and experiments. The optimal position for deployment of the magnetic field sensor is determined by scrutinizing the field uniformity and inspection sensitivity to SPC based on finite element simulations. After the optimal probe structure is chosen, quantitative evaluation and imaging of SPC are investigated. Signal/image processing algorithms for SPC evaluation are proposed. Through simulations and experiments, it has been found that the T-shaped probe together with the proposed processing algorithms is advantageous and preferable for profile recognition and depth evaluation of SPC.

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