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The high performance of deep learning in computer vision was first demonstrated in classification tasks. Many CNN models can provide good classification accuracy such as Vgg [28], ResNet [29], Xception [30], and so on. Some of them are applied as feature extractors in segmentation models. FCN replaces the fully connected layer in the classification model with deconvolution to upsample the pooled feature map to its original size, pioneered semantic segmentation. The application of deep learning in crack detection can be roughly divided into three types, methods based on classification [31], object detection [32], and semantic segmentation [33, 34]. Xue et al. [35] modified the last few deconvolution modules of FCN to adapt to the needs of crack segmentation. However, this FCN-based method may not be able to guarantee the accuracy of segmentation and maintain the original topological structure of the crack when facing the crack of complex structures.
where M refers to the number of categories, yic refers to the sign function (0 or 1), and pic refers to the predicted probability that the observed sample i belongs to category c. Thus, we can consider that the pixels in the image are learned equally with the cross-entropy loss function, and this kind of equality does not apply to the situation where the sample is extremely uneven. In coal crack CT images, the number of pixels corresponding to the crack is much smaller than that of the background. Taking the dataset we established as an example, the proportion of crack pixels in the whole image is less than 5%. Dice Loss [39] was proposed in 2016, designed to deal with scenarios where positive and negative samples are strongly imbalanced in semantic segmentation. Different from distribution-based cross-entropy loss, the Dice function is based on region and is used to calculate the similarity between two images. The Dice coefficient and Dice loss function can be formulated as follows:
A comparison of evaluation metrics of all these methods is shown in Table 4. As we can see, since the proportion of cracks in the images is very low, and the judgment error rate of image background pixels is low so that the total pixel accuracy of every method is not very different. However, the performance of different methods can still be judged from the remaining evaluation indicators. PSPNet and FCN may have good performance in semantic segmentation under natural conditions, but they do not perform well on the coal crack CT image dataset. U-net is designed to deal with medical images which have similarities with the images we used, so this model can have a nice performance. As the best performing comparison method, U-net achieved an Acc of 98.5%, mAcc of 94.2, MIoU of 86.5%, and a FWIoU of 97.2% which are 0.1%, 1.2%, 2.9%, and 0.5% lower than proposed method. A histogram comparison of the experimental results is shown in Figure 8.
Automatic crack detection is always a challenging task due to the influence of stains, shadows, complex texture, uneven illumination, blurring, and multiple scenes [2]. 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) [3], 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 [2].
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. [39] 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. [40] 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 [39], which used the same number of convolution kernels in all convolution layers, Nguyen et al. [41] 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 [42]. Eisenbach et al. [43] 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 [52], 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 [53]. 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. [3] 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. 2ff7e9595c
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