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Deep convolutional networks have been widely applied in large image classification [24,25]. Zhang et al. in  proposed a hybrid technique, which integrates CNN and boundary detection, and the performance can be much higher than traditional image processing techniques. Rodríguez et al. in  used segmentation technique to obtain crack information, and the crack is detected by  algorithm and threshold function. Wang et al. in  designed a Threshold-Adjusted multi-scale Convolutional Neural Networks based on contrast enhancement (TCNNBE) for roofs. Ren et al. in  employed the Scale-Invariant Feature Transform (SIFT) to detect cracks, and proposed the crack detection based on two-stage segmentation of SIFT-TBLS. Thérèse et al. in [30,31] both used deep learning, but they both introduced boundary detection. Actually, the boundary detection is performed to mark the area of the roof for the purpose of crack detection. Thus, the idea is similar to our developed method.
Some initial experiments were performed on the public available databases. As is shown in Table 7, it can be found that most of the methods need two or more images to construct a database, while our proposed method can build database directly by scanning the images. Furthermore, except the three datasets, the figures of all the other datasets are always lower than 0.1, it shows that the crack presents a unify geometry structure. Finally, it is also obvious that the recall is higher than the precision to make sure the crack information is extracted accurately. Detail information of the method is introduced in the following part.
At the same time, the comparison on the performance of image databases is performed. As is shown in Fig. 6, 2.2 and 0.1 are the average value of the grid, and the crack presents the higher value. Eventually, the performance is determined by experiment results. The global precision rate is higher than global recall rate to reach the best result.
In the past few decades, many researchers have performed structure health monitoring [12,13,14,15,16,17]. Yu et al. in  proposed an integrated system based on the robot for crack detection, which includes mobile manipulate and crack detection system. The mobile manipulate system is used to ensure distance from the objects, and crack detection system is employed to obtain pavement crack information. Oh et al. in  proposed bridge detection system, including a designed car, robot system, and machine vision system. Lim et al. in  designed a crack inspection system, which consists of three parts: mobile robot, vision system, and algorithm. The camera is mounted on the mobile robot to collect crack images; Laplacian of Gaussian algorithm is applied to extract crack information. 7211a4ac4a