In 2016, Zhang et al. [18] proposed a crack detection method based on deep learning. They trained a deep CNN based on supervised learning, proving the feasibility of combining deep learning with pavement crack recognition. In 2017, Zhao et al. [19] proposed a pavement crack detection method based on a CNN using images of different scales and taken at different angles for training, achieving the detection of cracks of various shapes. However, owing to road surface interference and noise, the detection accuracy of this system peaked at 82.5%. In 2017, Markus et al. developed the open dataset GAPs for the training of deep neural network and evaluated the pavement damage detection technology for the first time, which is of great significance [20, 21]. In 2018, Nhat-Duc et al. [22] established an intelligent method for the automatic recognition of pavement crack morphology; this study constructs a machine learning model for pavement crack classification that included multiple support vector machines and an artificial swarm optimization algorithm. Using feature analysis, a set of features is extracted from the image projection integral, which can significantly improve the prediction performance. However, the algorithm is complex and programming it becomes very difficult. In 2020, Zhaoyun Sun et al. [23] proposed a method to detect pavement expansion cracks with the improved Faster R-CNN, which can achieve accurate expansion crack location detection through the optimization model. The aforementioned studies only detect and classify pavement cracks and their location but cannot quantify certain crack characteristics, such as crack width and area. On the other hand, there are also many studies on crack segmentation. In 2018, Zhang and Wang [24] proposed CrackNet, which is an efficient architecture based on CNN to predict the class of each image pixel, but its network structure is related to input image size, which prevents the generalization of the method. In the same year, Sen Wang et al. [25] proposed to use the full convolutional networks (FCNs) to detect cracks and built the Crack-FCN model taking into account the shortcomings of the FCN model in the crack segmentation experiment and obtained a complete crack image. However, the highest accuracy obtained by their method is only 67.95%; thus, segmentation performance needs to be improved. In 2019, Piao Weng et al. [26] proposed a pavement crack segmentation method based on the VGG-U-Net model. It solves the problem of fracture in the crack segmentation result in complex background, but its training time is slightly longer and its efficiency is low. In 2020, Zhun Fan et al. [27] proposed an encoder-decoder architecture based on hierarchical feature learning and dilated convolution (U-HDN) detects cracks in an end-to-end manner. The U-HDN method can extract and fuse different context sizes and different levels of feature mapping, so it has high performance. In the same year, Zhun Fan et al. [28] proposed an ensemble of convolutional neural network based on probability fusion for automatic detection and measurement of pavement cracks, and the predicted crack morphology is measured by skeleton extraction algorithm. In summary, these previous studies only use the segmentation method, which cannot achieve accurate crack classification and location determination.
2020 Fusion Version 18 Crackedl
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