Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
Abstract
:1. Introduction
2. Review of Related Literatures
2.1. Image-Processing Analysis
2.2. Related Implementation
3. Methodology
3.1. Image Acquisition
3.2. Algorithm
3.3. Image Classifier
4. Results and Discussion
4.1. Analysis of Five Tonal Zone of Histogram
4.2. Image Quality Metrics
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CDF | Cumulative Distribution Function: this function of image processing presents the resulting image as a linear cumulative distribution function. |
CED | Canny Edge Detection: this edge detection operator uses a multi-stage algorithm to detect a wide range of edges in images. It locates the intensity gradients of the image and applies non-maximum suppression remove spurious response in the edges. |
CNN | Convolutional Neural Network: this provides the function of classification of images. |
ED | Edge Detection: this technique identifies points in a digital image with discontinuities. It sharpens changes in the image brightness. |
FN | False Negative: this provides the predicted “no”, indicating that non-defective concrete images are classified as “defective”. |
FP | False Positive: this provides the predicted “yes”, indicating that cracked images are classified inaccurately as “non-defective”. |
HE | Histogram Equalization: this function is a method in digital image processing that provides contrast adjustment using the histogram of the sampled image. |
LOG-ED | Laplacian of Gaussian Edge Detection: initially, this smoothens an image, and it then calculates the Laplacian. The process results in a double-edged image. It finds edges and then locates the zero-crossing between the double edges. |
ML | Machine Learning: this simply predicts outcomes of classifying the sampled images. Machine learning algorithms use historical data as input to predict new output values. |
PED | Prewitt Edge Detection: this operator is appropriate for detecting the magnitude and orientation of edges. It also has the same parameters as Sobel edge detection; however, it is easier to implement. |
RED | Robert’s Edge Detection: this operator is a straightforward and efficient approach to quantifying an image’s spatial gradient. The pixel value at a location in the produced image represents the estimated absolute magnitude value of the inputted image’s spatial gradient at that location. |
SED | Sobel Edge Detection: this operator works by calculating the gradient of the intensity of the digital image at each pixel within the image. It locates the direction of the maximum increase from light to dark and the rate of change in that direction. |
SNR | Signal-to-Noise Ratio: this function is a general metric for determining image quality. It is described as the relative strength of an aimed signal from a sample compared with the undesired background signal from noise. |
TN | True Negative: this provides the predicted “no”, indicating that cracked images are classified correctly as “defective”. |
TP | True Positive: this provides the predicted “yes”, indicating that non-defective concrete images are classified correctly as “non-defective”. |
Appendix A. Fundamental Definitions of Terms Used in This Article
Appendix A.1. Image-Processing Analysis
Appendix A.1.1. Fundamentals of Image Parameters
Appendix A.1.2. Graphical Representation of Digital Image
Histogram
CDF
Appendix A.1.3. Factors That Affect Image Quality
Appendix A.1.4. Image ED Methods for Image Segmentation
Image Segmentation
ED Techniques
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Brand/Company Name | FLIR E8 |
---|---|
Field of View | 45° × 34° |
Object Temperature Range | −20 °C to 250 °C |
Image Frequency | 9 Hz |
Thermal Sensitivity | <0.06 °C |
Accuracy | ±2 °C |
Thermal Palettes | Iron, Rainbow, Grayscale |
File Format | Radiometric JPG |
On-board Digital Camera | 640 × 480 |
Ordinary Camera | Thermal Camera | |
---|---|---|
images | ||
MSE | 23,432.0312 | 240.7319 |
PSNR | 4.4327 | 24.3155 |
SNR | 4.4053 | 24.2881 |
MS-SSIM | 0.0321 | 0.7885 |
BRISQUE | 43.4582 | 45.0100 |
NIQE | 15.1913 | 13.2069 |
PIQE | 63.0411 | 88.7166 |
Classifier Method | Percent Sensitivity | Percent Specificity | Percent Accuracy |
---|---|---|---|
CNN | 98% | 94.96 | 93.96 |
CNN-SVM | 99% | 95.3% | 98% |
Classifier Method | Percent Sensitivity | Percent Specificity | Percent Accuracy |
---|---|---|---|
CNN | 94.25 | 90% | 90% |
CNN-SVM | 97.55% | 93.65% | 93.96% |
Coefficient | CNN | CNN–SVM |
---|---|---|
Correlation | 0.9321 | 0.9981 |
Regression | 0.9629 | 0.9995 |
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Kim, B.; Choi, S.-W.; Hu, G.; Lee, D.-E.; Serfa Juan, R.O. Multivariate Analysis of Concrete Image Using Thermography and Edge Detection. Sensors 2021, 21, 7396. https://doi.org/10.3390/s21217396
Kim B, Choi S-W, Hu G, Lee D-E, Serfa Juan RO. Multivariate Analysis of Concrete Image Using Thermography and Edge Detection. Sensors. 2021; 21(21):7396. https://doi.org/10.3390/s21217396
Chicago/Turabian StyleKim, Bubryur, Se-Woon Choi, Gang Hu, Dong-Eun Lee, and Ronnie O. Serfa Juan. 2021. "Multivariate Analysis of Concrete Image Using Thermography and Edge Detection" Sensors 21, no. 21: 7396. https://doi.org/10.3390/s21217396