Decision edge crack serial

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The recall is the ratio of the number of positive samples correctly classified as positive over the total number of positive samples. F -measure combines precision and recall:.

Different edge detection operators were evaluated and compared. The code for image preprocess and edge detection is shown in the Appendix section.

Figure 5 shows a typical pavement surface image using the top-hat filtering, which is to remove the brightness in the background information from an image through opening operations. The color of the pavement image was reversed, the crack was light, and the background was dark as shown in Figure 5 b. Figure 5 c shows the brighter area in the image, which could be reduced by the top-hat transform. Figure 5 d then can be obtained by reducing the brighter area in the original image, and the cracks become clearer.

Generally, different edge detection methods have little effect on image segmentation. To further compare the results of image segmentation in more detail, the number of noise regions of different edge detection is calculated. For the pavement image shown in Figure 6 , the numbers of noise regions of the Prewitt, Sobel, LoG, and Canny operators are , , , and , respectively.

The corresponding numbers of crack regions are 21, 21, 21, and 23, respectively. Generally, the four operators obtained the same crack regions. The 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. The Sobel and LoG operators show similar image segmentations. The Prewitt and Canny operators have more noise in the image background. This is because the Sobel gradient operator and the spatial domain filter template in the LoG operator could reduce noise.

In addition, by comparing Figures 6 a and 6 f , it can be seen that preprocessing significantly improves segmentation effects. A large amount of noise remains without preprocessing. Figure 7 shows the structure of the decision tree model. Figure 8 shows the pattern recognition effect of the decision tree classifier with a pavement crack image containing transverse cracks.

It can be seen from Figure 8 d that the transverse crack regions in the image segmentation results are effectively classified and be separated from other types of cracks and noise.

In Figure 8 d , the noises are significantly reduced, which shows that the secondary denoising effect of the decision tree classifier. Generally, different types of cracks and the corresponding regions in the image are successfully extracted, except that part of the branches of the transverse cracks are identified as block cracks, and a very small amount of noise appears in the longitudinal crack classification image.

The proposed method achieved a precision of 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. This paper developed an improved Otsu method integrated with edge detection and decision tree classifier for cracking identification in asphalt pavements through image segmentation.

An image preprocessing approach including Gaussian function-based spatial filtering and top-hat transform is also proposed. The Gaussian function-based spatial filtering and top-hat transform significantly reduce the influence of poor shading and lighting effects and improve the image segmentation effects. The improved Otsu optimal global threshold segmentation method based on edge detection could effectively segment pavement crack images after valid preprocessing.

All the four edge detection operators have similar effects on segmentation. The Canny edge detection has a better effect on crack detection, obtaining more details of the edge and crack area, as well as more noises.

The decision tree classifier based on ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block cracks, which also has high calculation efficiency.

The proposed method achieved a fairly high precision, indicating a comparable performance on the crack detection based on 2D pavement surface images. However, it is still sensitive to the quality of images, especially when the pavement surface image contains extensive dirty spots, water, pavement texture, or shadows.

Recently, the high-resolution surface profile of pavement can be obtained with 3D cameras and laser line scanner. Those distress detection algorithms can be potentially directly used to process the data with depth information to evaluate pavement distress or texture. They can also be integrated with the deep learning-based methods to firstly identify the critical region to improve the calculation efficiency. In future studies, more types of cracks and other pavement distress including potholes and raveling could be potentially detected using the proposed methods with more pavement distress images for training the decision tree model.

Access to data is restricted as the dataset is from a third-party company and is under commercial confidentiality. The authors declare that they have no conflicts of interest regarding the publication of this paper. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors.

Read the winning articles. Journal overview. Special Issues. Received 19 Apr Revised 01 Jul Accepted 13 Jul Published 21 Jul Abstract The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers.

Introduction With the rapid development of the highway transportation infrastructure network and the increase of pavement service life, pavement distress including cracks, potholes, ruts, etc.

Methodology There are several assumptions for crack detection based on image processing [ 4 ]. Figure 1. Figure 2. Schematic of the gray model and its first and second derivatives. Figure 3. Prewitt and Sobel operator templates. Feature Description X1 Ratio of the major axis and the minor axis The ratio of the length in pixels of the major axis to the length in pixels of the minor axis of the ellipse that has the same normalized second central moments as the region, returned as a scalar X2 Angle between the horizontal axis and the major axis of the ellipse Angle between the horizontal the x -axis and the major axis of the ellipse that has the same normalized second central moments as the region, returned as a scalar X3 Area of the region Actual number of pixels in the region X4 Standard deviation of the gray level in the region Standard deviation value of the gray histogram of pixels in the region X5 Mean of the gray level in the region Mean value of the gray histogram of pixels in the region X6 Third-order moment of regional grayscale Third-order moment value of the gray histogram of pixels in the region.

Table 1. Figure 4. Samples for each crack class. Figure 5. Effect of top-hat conversion. Figure 6. Image segmentation using different operators. Figure 7. Figure 8. The effect of decision tree classifier. Happy Blogging! Back Previous Next. Latest Contents. AAA Local Backup not working as expected. Created by tbooth senegence.

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