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Automatic crack detection is always a challenging task due to the influence of stains, shadows, complex texture, uneven illumination, blurring, and multiple scenes . In the past decades, scholars have proposed a variety of image-based algorithms to automatically detect cracks on concrete surfaces and pavement. In the early studies, most of the methods are based on the combination or improvement of traditional digital image processing techniques (IPTs) , such as thresholding [4,5,6] and edge detection [7,8,9,10]. However, these methods are generally based on the significant assumption that the intensities of crack pixels are darker than the background and usually continuous, which makes these methods difficult to use effectively in the environment of complex background noise [11,12]. In order to improve the accuracy and integrity of crack detection, the methods based on wavelet transform [13,14] are proposed to lift the crack regions. However, due to the anisotropic characteristics of wavelets, they may not deal well with cracks with large curvatures or poor continuities .
In recent studies, several minimal path methods [15,16] have also been used for crack detection. Although these methods make use of crack features in a global view  and achieve good performance, their main limitation is that seed points for path tracking need to be set in advance , and the calculation cost is too high for practical application.
Unet , as a typical representative of semantic segmentation algorithm, has achieved great success in medical image segmentation. There are many similarities between pavement crack detection and medical image segmentation, so it is natural to apply Unet to pavement crack segmentation.
The patchwise detection method, which divides the original pavement images into many small patches, is adopted by more researchers due to its two advantages. First, more data can be generated, and second, the localization information of cracks can be obtained. Zhang et al.  proposed a six-layer CNN network with four convolutional layers and two fully connected layers and used their convolutional neural network to train 99 × 99 × 3 small patches, which were split from 3264 × 2248 road images collected by low-cost smartphones. The output of the network was the probability of whether a small patch was a crack or not. Their study shows that deep CNNs are superior to traditional machine learning techniques, such as SVM and boosting methods, in detecting pavement cracks. Pauly et al.  used a self-designed CNN model to study the relationship between network depth and network accuracy and proved the effectiveness of using a deeper network to improve detection accuracy in pavement crack detection based on computer vision. In contrast with , which used the same number of convolution kernels in all convolution layers, Nguyen et al.  used a convolution neural network with an increased number of convolution kernels in each layer because the features were more generic in the early layers and more original dataset specific in later layers . Eisenbach et al.  presented the GAPs dataset, constructed a CNN network with eight convolution layers and three full connection layers, and analyzed the effectiveness of the state-of-the-art regularization techniques. However, its network input size was 64 × 64 pixels, which was too small to provide enough context information. The same problem also existed in [44,45,46].
Zhang et al. put forward CrackNet , which is an earlier study on pixel-level crack detection based on CNN. The prominent feature of CrackNet is using a CNN model without a pooling layer to retain the spatial resolution. Fei et al. have upgraded it to Cracknet-V . While CrackNet and its series versions perform well, they are primarily used for 3D road crack images, and their performances on two-dimensional (2D) road crack images have not been validated. Fan et al.  proposed a pixel-level structured prediction method using CNN with full connections (FC) layers, but it has the disadvantage that it requires a long inference time for testing.
The CFD dataset, published in , consists of 118 RGB images with a resolution of 480 × 320 pixels. All of the images were taken using an iPhone5 smartphone on the road in Beijing, China, and can roughly reflect the existing urban road conditions in Beijing. These crack images have uneven illumination and contain noise such as shadows, oil spots, and lane lines, and most cracks in these images are thin cracks, which make crack detection difficult. We randomly divided 70% of the dataset (82 images) for training and 30% of the dataset (36 images) for testing.
The Crack500 dataset, shared by Yang et al. in the literature , contains 500 original images with a resolution of 2560 × 1440 collected at the main campus of Temple University. Each original image was cropped into a non-overlapping image area of 640 × 360, resulting in 1896 training images, 348 validation images, and 1123 test images. These images are characterized by low contrast between cracks and background, as well as noise such as oil pollution and occlusions, which increase the difficulty of detection.
The DeepCrack dataset  contains 537 crack images, including both concrete pavement and asphalt pavement, with complex background and various crack widths, ranging from 1 pixel to 180 pixels. We kept the same data split as the original paper, with 300 images for training and 237 images for testing.
Figure 3 shows the crack detection results of six typical input images of our method and the three methods to be compared. The first column is the original input crack image, the second column is the label image corresponding to the first column image, and the next four columns are the predicted output images of the four comparison algorithms. As can be seen from Figure 3, all these algorithms could detect the rough crack profile. However, in terms of details, all three algorithms, FCN, Unet, and SegNet, had false detection and missing cracks resulting in discontinuity of cracks to a varying degree. Our algorithm was obviously better than the three algorithms, with the least false detection and missing cracks, and the closest to the ground truth.
The micro-structure of the carbon steel side shows a narrow region with a width of approximately 120 μm near the fusion boundary (FB), and obvious dendrite structures are remaining, containing the type I grain boundary perpendicular to the FB line, and type II grain boundary parallel to the FB line, in addition to high hardness and high residual strain, as shown in Figure 7. The carbon element is diffused from the bottom layer into the cladding material, and the carbon steel side is decarbonized during the formation of the clad rebar. With the increase of the carbon content in the base metal, the width of the decarbonization zone expands to 50 µm~60 µm. Moreover, this region becomes the dilution zone. This unique grain boundary in the fusion boundary of carbon steel results from the obvious change of micro-structure and composition, stressing the fact that the corrosion crack (SCC) is easily expanded along this path. The large angle grain boundary of type II has a higher SCC sensitivity than type I [27,28]. 2b1af7f3a8