Abstract
During the phase of periodic survey, sealed crack and crack in asphalt pavement surface should be detected accurately. Moreover, the capability of identifying these two defects can help reduce the false-positive rate for pavement crack detection. Because crack and sealed crack are both line-based defects and may resemble each other in shape, this study puts forward an innovative method based on computer vision for detecting sealed crack and crack. This method is an integration of feature extraction based on image processing and metaheuristic optimized machine learning. Image processing is used to compute features that characterize visual appearance and texture of the pavement image. Subsequently, Salp Swarm Algorithm integrated with multiclass support vector machine is employed for pattern recognition. Based on experimental results, the newly developed method has achieved the most desired predictive performance with an accuracy rate = 91.33% for crack detection and 92.83% for sealed crack detection.
Highlights
Asphalt pavement is one of the most important components of transportation network
It is noted that when the value of the Gaussian function variance is fixed, the final filter response is a combination of Gaussian Steerable Filter (GSF) with a set of orientation β. e value of β is often selected from a set of angles, i.e., {0, π/4, 2π/4, 3π/4π}. e final GSF response at the pixel location (x, y) within an image I can be obtained via the following formula: R(x, y) F(x, y, σ, β) ∗ I(x, y), (4)
Analyses on attractive and repulsive relationship is performed with a local structure of the size 3 × 3 pixels for all of the pixels in an image sample
Summary
Asphalt pavement is one of the most important components of transportation network. Nowadays, intensive traffic loads and inclement weather conditions expedite the deterioration rate of asphalt pavement roads. It is a requirement that the pavement management systems receive accurate evaluation results of asphalt pavement conditions in a timely manner. [16], accurate and timely detection of crack is essential for the process of asphalt pavement condition survey. Such detection can significantly help to conserve maintenance budgets, establish proper plans of maintenance/rehabilitation, and assure the longterm serviceability of asphalt pavements [17]. Erefore, this study puts forward an automatic method for categorizing crack and sealed crack image samples by the utilization of image processing and image texture analysis. E combination of the employed image analysis methods including the Gaussian steerable filters, projection integrals, and texture descriptors aim at tackling the aforementioned challenges pointed out by Zhang et al [20]. Experimental results and discussion are reported in the fifth section, followed by the section of concluding remarks
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