Abstract
The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grayscale histogram, making it difficult to segment. This paper developed an improved Otsu method integrated with edge detection and a decision tree classifier for cracking identification in asphalt pavements. An image preprocessing approach including Gaussian function‐based spatial filtering and top‐hat transform is firstly proposed to reduce the influence of poor shading and lighting effects significantly. Four edge detection operators including Prewitt, Sobel, Gauss–Laplace (LoG), and Canny are evaluated. The Canny edge detection has demonstrated outstanding performance in crack detection; this algorithm helps to obtain more details of both cracks and noises. The Sobel and LoG operators show similar image segmentation and retain fewer noises. The decision tree classifier based on the ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block ones.
Highlights
With the rapid development of the highway transportation infrastructure network and the increase of pavement service life, pavement distress including cracks, potholes, ruts, etc., increase rapidly. e detection and treatment of pavement distress have gradually become an important focus in the field of pavement engineering
The four operators obtained the same crack regions. e Canny edge detection has a better effect on crack detection than the other methods, obtaining more details of the edge and crack area, while retaining more noises. e Sobel and Laplace of Gauss (LoG) operators show similar image segmentations. e Prewitt and Canny operators have more noise in the image background. is is because the Sobel gradient operator and the spatial domain filter template in the LoG operator could reduce noise
Because the area of the pavement crack is too small, comparing with the image background, the crack only accounts for a very small portion in the grayscale histogram and the pixels are highly concentrated, making it difficult to split effectively. is paper developed an improved Otsu method integrated with edge detection and decision tree classifier for cracking identification in asphalt pavements through image segmentation
Summary
With the rapid development of the highway transportation infrastructure network and the increase of pavement service life, pavement distress including cracks, potholes, ruts, etc., increase rapidly. e detection and treatment of pavement distress have gradually become an important focus in the field of pavement engineering. Four edge detection methods including the Prewitt operator, Sobel operator, Laplace of Gauss (LoG) method, and Canny were evaluated with the improved optimal global threshold method in segmenting pavement images. E Otsu method is used to select the optimal threshold value of the edge region in the pavement image for the segmentation, and the decision tree is adopted to further eliminate the noise from cracks. E Canny operator uses the firstorder directional derivative of the two-dimensional Gaussian function in any direction to reduce noise and compare it with the spatial convolution of the input image f(x, y) to suppress noise and find the maximum gradient to detect the edge of the image. (4) For each gray-level probability value obtained in step (3), use equation (11) to calculate the maximum between-class variance and obtain the best threshold K to segment image f(x, y).
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