GAN–XGB–cavity: automated estimation of underground cavities’ properties using GPR data

• Published in: Neural computing & applications Vol. 35; no. 25; pp. 18357 - 18376
• Main Authors: Tran, Van Phuc, Nguyen, Son Dong, Lee, Hyun Jong, Tran, Thai Son, Elipse, Carlo
• Format: Journal Article
• Language: English
• Published: London Springer London 01-09-2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classifiers; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Decision making; Generative adversarial networks; Ground penetrating radar; Height; Holes; Image processing; Image Processing and Computer Vision; Image segmentation; Machine learning; Original Article; Pavements; Probability and Statistics in Computer Science; Rehabilitation; Root-mean-square errors; Support vector machines; Deep learning; DCGAN; Ground-penetrating radar; Hyperbola fitting; Pix2Pix; Cavity property estimation
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-08655-1

Recently, various techniques have been implemented to investigate the cavity beneath the pavement using ground-penetrating radar (GPR) data. These techniques usually focus on the determination of the height of the cavity and the depth at which it is located, which play a vital role in the agencies’ decision-making on the appropriate rehabilitation method for the pavement. Image processing, a relatively advanced technique that utilizes the image generated from the GPR data, has been used for this purpose. However, it is still time-consuming and yields low accuracy. As a solution, this study proposes a machine learning (ML)-based method to estimate cavity properties, such as depth and height, from GPR images with high precision in a significantly reduced time. In this study, a deep convolutional generative adversarial networks-based hyperbolic fitting algorithm, called GAN–cavity, was developed based on the Pix2Pix model to identify multi-shape signature segmentation from the GPR data. The GAN–cavity was trained and tested using 1920 and 480 images, respectively, and the model performed well with an average F1–score of 83.1 per cent. Consequently, an extreme gradient boosting (XGBoost) algorithm was used to predict the cavity depth and height based on the GAN–cavity results. Moreover, the developed model (XGB–cavity classifier) can classify the cavity’s height into three classes: small, medium, and large. The XGBoost model XGB–cavity regressor used 341 and 87 cavities for training and testing, respectively, yielding regression scores (R2) and root mean squared error (RMSE) of 0.88 and 3.28 cm in cavity depth prediction, respectively. By applying the support vector machine synthetic minority oversampling technique (SVM–SMOTE) algorithm to overcome the data imbalance, the XGB–cavity classifier model obtained a high accuracy of 0.89 with precision–avg, recall–avg, and F1–score–avg of 0.83, 0.83, and 0.83, respectively. Based on the results, the proposed method (GAN–XGB–cavity) of combining the GAN–cavity and XGB–cavity can successfully identify and fit multi-shape segmentation for depth estimation and height classification of cavities.