# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
###### Abstract
Therefore, scholars at home and abroad have proposed many deep learning-based methods for pavement crack recognition, such as migration learning-based methods [13; 14; 15], machine learning [16; 17; 18], generative adversarial network (GAN)-based methods [19; 20], convolutional-recurrent neural network (CNN-RNN)-based methods [21; 22], and so on. Meanwhile, some research institutions and companies at home and abroad have started to invest a lot of human and material resources in road image acquisition and data annotation to better support the training and optimization of deep learning algorithms. A mask-region-based convolutional neural network (R-CNN) is used in crack detection, which can automatically detect and segment small cracks in asphalt pavements at the pixel level. Simulations were performed using Gprma software (version V3.1.6) and combined with field inspection to determine the characteristics of cracks in ground-penetrating radar images of asphalt pavements and the relationship between vertical crack width and area in ground-penetrating radar images [23]. [PERSON] et al. [24] trained a deep convolutional neural network based on migration learning to classify pavement images and proposed a block-based threshold segmentation method to segment cracks at the pixel level. However, the method needed to be improved in terms of pixel-level recognition accuracy for cracks. [PERSON] [25] proposed a new neural network model based on the codec network and attention mechanisms to detect and evaluate road cracks, which could extract crack pixels more accurately and efficiently. However, the model had high requirements for equipment, which was not conducive to practical engineering applications, and the accuracy rate needed to be improved. [PERSON] [26] proposed a road image crack detection network, RAO-UNet. This network used a codec and an image frequency relationship based on a residual attention model to detect cracks. However, the method is time-consuming for detecting cracks. Moreover, the cracks identified by this method were prone to fracture [PERSON] [27] proposed an improved DeepLabv3+ segmentation network for crack recognition. The network had a multi-scale attention module that was able to assign different weights to different feature mappings. This method consumed more computational resources at runtime and had a larger weight file, and the accuracy rate needed to be improved. The deep residual convolutional neural network (ParallelResNet) proposed by [PERSON] [28] was able to identify complex cracks. The network was tested only on still images and not on video streams and was more dependent on the accuracy of the dataset labeling. [PERSON] et al. [29] proposed a new crack identification network by combining feature pyramids and hierarchical boosting networks. [PERSON], [PERSON] et al. [30] proposed a CrackU-net for pixel-level crack recognition networks. Experimental results show that the network outperforms traditional methods and full convolutional networks (FCN) for crack identification. However, the network relied on a large number of datasets and cannot be trained on smaller datasets, and the segmentation details were not well-handled. [PERSON], [PERSON] [31] proposed a crack segmentation network called CrackSegNet. The network was more generalized than previous segmentation methods. However, the recognition accuracy of this network was not high, and the edge details were blurred. [PERSON], [PERSON] [32] proposed a crack recognition network based on a multi-feature layer convolutional neural network. Although this method improved the recognition accuracy, it did not make good use of the contextual features of the cracks. [PERSON], [PERSON] [33] proposed a method to detect concrete cracks using the U-Net network. The network is capable of identifying crack locations from the input raw images under various conditions. It has high efficiency and robustness.