A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine

Abstract

To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface. Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image samples into two categories of no surface void (negative class) and surface void (positive class). Furthermore, to assist the SVM model training phase, the state-of-the-art history-based adaptive differential evolution with linear population size reduction (L-SHADE) is utilized. The hybrid intelligence approach, named as L-SHADE-SVM-SVD, has been developed and complied in Visual C#.NET framework. Experiments with 1000 image samples show that the L-SHADE-SVM-SVD can obtain a high prediction accuracy of roughly 93%. Therefore, the newly developed model can be a promising alternative for construction inspectors in concrete quality assessment.

Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

“…In order to support the surface void classification process, texture feature which is insensitive to illumination is always considered. Following Hoang and Nguyen's research work [ 17 ], the Gabor filter and gray-level run-length methods are employed for feature extraction.…”

“…It has been stated that the tuning parameters of the Gabor filter including the orientation angles and the radial frequencies must be specified in order to better recognize texture [ 22 ]. As suggested in the previous work of Hoang and Nguyen [ 17 ], the orientation angles including 0°, 45°, 90°, and 135° can be used. As to the radial frequency u 0 , it can be set to , , ,…, , where N w represents the number of pixels and is a power of 2.…”

“…Given a certain direction in an image (e.g., 0°, 45°, 90°, and 135°), a run-length matrix p ( i , j ) is composed of the run-length j of gray level i . Based on the run-length matrix, Hoang and Nguyen [ 17 ] employed a variety of statistics to describe texture in the image. In turn, these statistics include short-run emphasis (SRE), long-run emphasis (LRE), gray-level nonuniformity (GLN), run-length nonuniformity (RLN), run percentage (RP), low gray-level run emphasis (LGRE), high gray-level run emphasis (HGRE), short-run low gray-level emphasis (SRLGE), short-run high gray-level emphasis (SRHGE), long-run low gray-level emphasis (LRLGE), and long-run high gray-level emphasis (LRHGE), which are listed as follows: where M and N p denote the number of gray levels and pixels, respectively.…”

“…In addition, different classification models (namely, classifiers) for discriminating whether there are bugholes or not on the concrete surface are always presented. Typically, Hoang and Nguyen [ 17 ] employed the Gabor filter and gray-level run lengths to generate a 108-dimensional texture feature derived from a concrete surface. Besides, a support vector machine (SVM) with its parameters optimized by employing an adaptive differential evolution with linear population size reduction was utilized to classify images of concrete surfaces into ones with surface voids and the others without surface voids.…”

“…In this paper, we propose a variable selection method for automatic classification of surface voids from concrete images. Variables which are considered as the components of the 108-dimensional texture feature [ 17 ] are selected using a presented cumulative voting strategy. After that, ensemble classification is made on the selected variables to distinguish the images of concrete surfaces with bugholes and those without surface voids.…”