Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression

Hoang, Nhat‐Duc

doi:10.1016/j.autcon.2019.102843

Cited by 70 publications

(28 citation statements)

References 50 publications

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“…Given an image sample I, the first-order histogram P(I) can be computed. Accordingly, the mean (μ c ), standard deviation (σ c ), skewness (S c ), kurtosis (K c ), entropy (E c ), and range (R c ) of the three color channels (R, G, and B) can be calculated [29,50].…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

“…Based on the recommendations of Haralick et al [51], the GLCM can be constructed with r � 1 and θ � 0°, 45°, 90°, and 135°. Accordingly, for each matrix, four indices of angular second moment (AM), contrast (CO), correlation (CR), and entropy (ET) can be computed as follows [29,55]:…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

“…A concrete spalling detection model for metro tunnel from point cloud that employs a roughness descriptor has been developed by Wu et al [24]. Hoang [29] presents an image processing approach for automatic detection of concrete spalling using machine learning algorithms and image texture analysis. Nevertheless, this model focused on machine learning-based texture discrimination and was not capable of isolating the entire individual spall object.…”

Section: Introductionmentioning

confidence: 99%

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Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

Hoang

2020

Advances in Civil Engineering

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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.

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Section: Advances In Civil Engineeringmentioning

confidence: 99%

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

Hoang

2020

Advances in Civil Engineering

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“…SGD is used to improve LR both in the sense of minimizing the error function and in keeping the computation time and complexity low. The technique is used in several domains, from healthcare (Manogaran & Lopez, 2018) to constructions (Hoang, 2019), as well as in economics (including loan classification), but we were unable to find an implementation using LR, as most focus on complex algorithms.…”

Section: Own Contributionmentioning

confidence: 99%

Will they repay their debt? Identification of borrowers likely to be charged off

Căplescu

Panaite

Pele

et al. 2020

Management &Amp; Marketing. Challenges for the Knowledge Society

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Recent increase in peer-to-peer lending prompted for development of models to separate good and bad clients to mitigate risks both for lenders and for the platforms. The rapidly increasing body of literature provides several comparisons between various models. Among the most frequently employed ones are logistic regression, Support Vector Machines, neural networks and decision tree-based models. Among them, logistic regression has proved to be a strong candidate both because its good performance and due to its high explainability. The present paper aims to compare four pairs of models (for imbalanced and under-sampled data) meant to predict charged off clients by optimizing F1 score. We found that, if the data is balanced, Logistic Regression, both simple and with Stochastic Gradient Descent, outperforms LightGBM and K-Nearest Neighbors in optimizing F1 score. We chose this metric as it provides balance between the interests of the lenders and those of the platform. Loan term, debt-to-income ratio and number of accounts were found to be important positively related predictors of risk of charge off. At the other end of the spectrum, by far the strongest impact on charge off probability is that of the FICO score. The final number of features retained by the two models differs very much, because, although both models use Lasso for feature selection, Stochastic Gradient Descent Logistic Regression uses a stronger regularization. The analysis was performed using Python (numpy, pandas, sklearn and imblearn).

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“…Moreover, the LR model structure is easy to interpret. This machine learning method has also been successfully applied in various recent applications [53][54][55][56].…”

Section: Gray-level Run Lengths (Glrl) First Proposed Bymentioning

confidence: 99%

Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis

2019

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Recognition of spalling on surface of concrete wall is crucial in building condition survey. Early detection of this form of defect can help to develop cost-effective rehabilitation methods for maintenance agencies. This study develops a method for automatic detection of spalled areas. The proposed approach includes image texture computation for image feature extraction and a piecewise linear stochastic gradient descent logistic regression (PL-SGDLR) used for pattern recognition. Image texture obtained from statistical properties of color channels, gray-level cooccurrence matrix, and gray-level run lengths is used as features to characterize surface condition of concrete wall. Based on these extracted features, PL-SGDLR is employed to categorize image samples into two classes of “nonspall” (negative class) and “spall” (positive class). Notably, PL-SGDLR is an extension of the standard logistic regression within which a linear decision surface is replaced by a piecewise linear one. This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. Experiments with 1240 collected image samples show that PL-SGDLR can help to deliver a good detection accuracy (classification accuracy rate = 90.24%). To ease the model implementation, the PL-SGDLR program has been developed and compiled in MATLAB and Visual C# .NET. Thus, the proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings.

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Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression

Cited by 70 publications

References 50 publications

Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

Image Processing‐Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

Will they repay their debt? Identification of borrowers likely to be charged off

Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis

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