Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models
This study aims at proposing a computer vision model for automatic recognition of localized spall objects appearing on surfaces of reinforced concrete elements. The new model is an integration of image processing techniques and machine learning approaches. The Gabor filter supported by principal com...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8829715 |
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| _version_ | 1832567908140580864 |
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| author | Nhat-Duc Hoang |
| author_facet | Nhat-Duc Hoang |
| author_sort | Nhat-Duc Hoang |
| collection | DOAJ |
| description | This study aims at proposing a computer vision model for automatic recognition of localized spall objects appearing on surfaces of reinforced concrete elements. The new model is an integration of image processing techniques and machine learning approaches. The Gabor filter supported by principal component analysis and k-means clustering is used for identifying the region of interest within an image sample. The binary gradient contour, gray level co-occurrence matrix, and color channels’ statistical measurements are employed to compute the texture of the extracted region of interest. Based on the computed texture-based features, the logistic regression model trained by the state-of-the-art adaptive moment estimation (Adam) is utilized to establish a decision boundary that delivers predictions on the status of “nonlocalized spall” and “localized spall.” Experimental results demonstrate that the newly developed model is able to achieve good detection accuracy with classification accuracy rate = 85.32%, precision = 0.86, recall = 0.79, negative predictive value = 0.85, and F1 score = 0.82. Thus, the proposed computer vision model can be helpful to assist decision makers in the task of the periodic survey of structure heath condition. |
| format | Article |
| id | doaj-art-f75d7f73656740d79a0792bb64973d39 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-f75d7f73656740d79a0792bb64973d392025-02-03T01:00:20ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88297158829715Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression ModelsNhat-Duc Hoang0Institute of Research and Development, Duy Tan University, Da Nang 550000, VietnamThis study aims at proposing a computer vision model for automatic recognition of localized spall objects appearing on surfaces of reinforced concrete elements. The new model is an integration of image processing techniques and machine learning approaches. The Gabor filter supported by principal component analysis and k-means clustering is used for identifying the region of interest within an image sample. The binary gradient contour, gray level co-occurrence matrix, and color channels’ statistical measurements are employed to compute the texture of the extracted region of interest. Based on the computed texture-based features, the logistic regression model trained by the state-of-the-art adaptive moment estimation (Adam) is utilized to establish a decision boundary that delivers predictions on the status of “nonlocalized spall” and “localized spall.” Experimental results demonstrate that the newly developed model is able to achieve good detection accuracy with classification accuracy rate = 85.32%, precision = 0.86, recall = 0.79, negative predictive value = 0.85, and F1 score = 0.82. Thus, the proposed computer vision model can be helpful to assist decision makers in the task of the periodic survey of structure heath condition.http://dx.doi.org/10.1155/2020/8829715 |
| spellingShingle | Nhat-Duc Hoang Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models Advances in Civil Engineering |
| title | Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models |
| title_full | Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models |
| title_fullStr | Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models |
| title_full_unstemmed | Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models |
| title_short | Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models |
| title_sort | image processing based spall object detection using gabor filter texture analysis and adaptive moment estimation adam optimized logistic regression models |
| url | http://dx.doi.org/10.1155/2020/8829715 |
| work_keys_str_mv | AT nhatduchoang imageprocessingbasedspallobjectdetectionusinggaborfiltertextureanalysisandadaptivemomentestimationadamoptimizedlogisticregressionmodels |