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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.
The model was improved to optimize the hyperparameters in the network. After comparing the segmentation effect under different hyperparameter settings, the activation function was set as the sigmoid function and the SGD optimizer was selected to optimize the network training. The PR curves of the pavement crack segmentation model before and after improvement for the three types of crack images are shown in Figure 13. As can be seen, the upper right convexity of the PR curve after improvement is more evident than before the improvement, indicating a better performance by the improved model.
The deep convolutional network is trained by means of supervised learning. By extracting the morphological features of the cracks and continuously comparing them with the tag values to calculate the loss, the parameters of each network layer are continuously adjusted to finally reach the state of small loss and accurate judgment. With the deepening of convolutional layers in the network model, the model has more powerful feature extraction capabilities and can therefore identify and detect more abstract information. At this stage of the experiment, the crack characteristics learned by the deep convolutional network are analyzed and studied, and the characteristics of the three crack types at different depths are displayed by means of feature visualization.
The role of the optimizer in the deep neural network is to update and calculate the network parameters that affect model training and model output so as to make them approximate an optimal value and to minimize the loss function. For deep convolutional neural networks, choosing an appropriate optimizer plays a decisive role in the final recognition accuracy of the model. In this study, Adam, SGD, AdaGrad, AdamW, and Nadam were used to train and test the model. The test results are shown in Table 7. When training on crack data, although the Adam optimizer has a relatively fast training speed, its crack prediction accuracy is not the best. Whereas differences in training times were small, accuracy values clearly differ. Therefore, SGD was selected as the model optimizer.
In this research, a crack identification method based on a deep convolutional neural network fusion model is proposed. The strategy for model optimization was carried out through repeated experiments, and the model hyperparameters were optimized, effectively improving its pavement crack identification accuracy. In summary, the following conclusions can be drawn from this research:(1)To achieve accurate crack classification and segmentation, we propose a fusion model. The SSD network is used as the detection model and the U-Net network is used as the segmentation model, which can achieve crack classification and segments the cracks in the detection box at the same time. Moreover, crack length, width, and area parameters are calculated using the crack-segmented binary image. This method not only ensures the accuracy of crack number but also calculates crack parameters, which can prevent the misjudgment of crack number caused by using only a single model.(2)The SSD network model was proposed as the pavement crack classification and detection network. We improved the SSD model by replacing the VGG16 feature extraction network with the deep residual network. Experimental results show that the mAP of the improved model is 6.5% higher than that of the former model, which indicates that the classification and detection level of pavement cracks can be improved by optimizing the network. We made the same improvement to the U-Net model. By joining the segmentation model behind the detection model, we can solve the problem of inaccurate crack location in the pavement classification detection network. Results show that the precision of the improved segmentation model is 6.9% higher than that of the former. Therefore, the proposed fusion model has value in the field of pavement crack identification and classification.(3)Compare and selecte the learning rate, activation function, optimizer, and other parameters of the model. Experimental results demonstrate that the proposed model not only improves performance when compared to the original model but also achieves higher accuracy, which has certain practical application value.
The proposed approach has two key advantages over previous studies. First, the proposed self-adaptive multi-peak algorithm can effectively extract the damage features of central wavelength shifts and the experimental results show that the performance of the proposed algorithm is superior to that of other traditional peak seeking algorithms in pm accuracy and self-adaption. Second, the different characteristic variation intervals can be associated with different crack propagation states by a detailed analysis. Additionally, the damage feature proposed in this paper represents the average strain loaded in the gratings and has a connection with the crack length.
The traditional peak seeking algorithms such as the maximum and extremum algorithm [12], first-order derivative [13] and the thresholding methods [14] have many limitations, such as poor anti-noise performance, low computational accuracy. The peak searching methods based on curve fitting, including Gaussian fitting [15], polynomial fitting [16], three-point peak detection [17] and the centroid algorithm [18] have high peak detection precision, but their performances are affected by spectral types, especially for deformation asymmetry spectra. To overcome the mentioned problems, other algorithms were developed, however, for the super-Gaussian model [19] it is difficult to choose the modified function parameters and the Monte Carlo methods [20] can hardly meet the precision demands owing to their nonlinear characteristics. Additionally, several optimization algorithms such as genetic algorithm [21], self-adaptive neighborhoods search [19], tree search [22], and dynamic multi-swarm particle optimizer algorithm [23] have been proposed to deal with the multi-peak detection problem. However, their iterations take a long time to find the optimum solution and their computational complexity is high. Therefore, it is desirable to develop a multi-peak seeking method to extract the central wavelength shifts from the FBG reflection spectrum. 2b1af7f3a8
