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The traditional method for detecting cracks in concrete bridges has the disadvantages of low accuracy and weak robustness. Combined with the crack digital image data obtained from bending test of reinforced concrete beams, a crack identification method for concrete structures based on improved U-net convolutional neural networks is proposed to improve the accuracy of crack identification in this article. Firstly, a bending test of concrete beams is conducted to collect crack images. Secondly, datasets of crack images are obtained using the data augmentation technology. Selected cracks are marked. Thirdly, based on the U-net neural networks, an improved inception module and an Atrous Spatial Pyramid Pooling module are added in the improved U-net model. Finally, the widths of cracks are identified using the concrete crack binary images obtained from the improved U-net model. The average precision of the test set of the proposed model is 11.7% higher than that of the U-net neural network segmentation model. The average relative error of the crack width of the proposed model is 13.2%, which is 18.6% less than that measured by using the ACTIS system. The results indicate that the proposed method is accurate, robust, and suitable for crack identification in concrete structures.

Cracks are one of the most serious defects in concrete structures, because when they are developed, they tend to reduce the effective loading area. It not only affects the appearance of structures, but also causes corrosion of the internal steel bars and accelerates the aging of structures, thereby affecting the bearing capacity and safety of structures. In addition, cracks originated at the surface, and it is difficult to detect them visually if the crack width is small. Therefore, detecting cracks quickly and accurately is an extremely important means for inspection and safety assessment in concrete bridges. The most widely applied techniques for detecting cracks in concrete are scanning electron microscopy and optical fluorescent microscopy. With the advancement of science and technology, digital image processing technology, as a powerful tool for crack detection, has been widely used in concrete bridges.

Detection of surface cracks in building structures is using the digital image processing technique with image thresholding. Tong et al. [1] used the gray difference between the crack area and the background and the gray threshold segmentation method to extract the crack. However, the segmentation effect of this method is poor when it is applied to the image of bridge crack with complex background. Hoang [2] proposed a surface crack detection in building structures using image processing technique with an improved Otsu method for image thresholding. He established an intelligent model for automatic crack recognition and analyses. The model using the improved Otsu method could effectively eliminate noisy pixels and noncrack pixels in crack images. However, the detection effect of the model was not good when the background pixel value and the noise pixel value were lower than the set threshold. Based on histogram estimation and shape analysis, Xu et al. [3] used the multiscale segmentation method to perform threshold segmentation for subblock images with different scales. The location of cracks could be determined according to the linear characteristics of cracks. It could obtain more crack information by using the multiscale segmentation method, but it would introduce more noise.

In recent years, neural network technology has been widely used in the processing of crack images. Zhang et al. [8, 9] used convolutional neural network (CNN) to predict whether a single pixel in a crack image belongs to a crack. The proposed algorithm could reflect the details of cracks. However, it needs manually set feature extractor for preprocessing. The size of images has a great influence to the setting of networks. Cha et al. [10] proposed a defect detection algorithm based on Fast Region Convolutional Neural Networks (Fast R-CNN) and compared it with the traditional edge detection algorithms of Canny and Sobel. The results indicated that the algorithm could detect more types of defects. However, the algorithm took a lot of time to process images and could not get a complete crack image. Chen et al. [11] used the convolutional neural network and NB-CNN network fused with naive Bayesian data for fracture detection. The advantage of this algorithm is that it can detect tiny cracks, but this method can only detect the location of cracks and cannot extract cracks. Li et al. [12] designed a CNN with dual-partition output based on improved Google-net convolutional neural network. This method could extract crack feature information of images, but it could not locate the extracted crack information to the original image location. Yang et al. [13] used the semantic segmentation method based on fully convolutional network (FCN) to detect images of cracks. The detection method could extract relatively complete crack images. Its training time was short. However, its image information loss was large, and the spatial level information location of crack pixels was not accurate enough. Zhu et al. [14] proposed a crack identification algorithm for U-net convolutional neural network, using U-net networks as the front end to extract the crack, and then using threshold method and Dijkstra connection to extract the crack accurately. However, this method was still difficult to solve the problem that feature resolution degradation caused by continuous pooling. An Atrous Spatial Pyramid Pooling (ASPP) module was proposed by Chen et al. [15, 16], which probed convolutional features at multiple scales, with image-level features encoding global context and further boosting performance. It used Atrous convolution in cascade or in parallel to capture multiscale context by adopting multiple Atrous rates, which could increase the details of image features and enhance the effect of dense prediction.

In order to improve the accuracy of crack detection of reinforced concrete structures, a method for identifying cracks of concrete beams based on improved U-net convolutional networks is proposed in this article. Firstly, a bending test of concrete beams is conducted to collect crack images. Secondly, datasets of crack images are obtained by using data augmentation technology. Selected cracks are marked. Thirdly, based on the U-net neural networks, an improved inception module and an Atrous Spatial Pyramid Pooling (ASPP) module are added in the improved U-net model to reduce the data loss in the process of pooling and improve the accuracy of multifeature fusion, which is the innovation of this paper. The new Loss Function Dice Loss is used in the model to improve the sensitivity of the network to pixels of cracks. Finally, the widths of cracks are identified using MATLAB image processing technology.

Flowchart of the crack detection is shown in Figure 2. Using 300 images to train a deep learning neural network might be overfitting, so it is necessary to divide the original dataset into a training dataset and a test dataset, augment the training dataset, and verify the model on the test dataset. Data augmentation technology includes horizontal mirroring (equation (1)) and vertical mirroring (equation (2)).

U-net convolutional neural networks are improved fully convolutional neural networks. They make full use of the abstract features obtained by the deep network and the image information contained in the shallow networks. They adopt the method of copy and superposition for feature fusion. Therefore, they could realize automatic segmentation of images effectively and accurately. U-net convolutional neural networks are mainly used in medical image segmentation, which indicates that, in the case of a small amount of deep learning data, the semantic segmentation accuracy of U-net is relatively high, so that it can reduce the workload of concrete detection and improve the efficiency of concrete crack detection. Schematic diagram of U-net convolutional neural networks is shown in Figure 5.

Training, verifying, and testing are performed on the 64-bit window 10 platform equipped with i7700u CPU, 8G memory, and GTX1060 GPU. The network model is established based on the PyTorch deep learning framework and trained using the GPU. The crack images are collected in the bending test of concrete beams. 1000 crack datasets are got by using the data augment technology. In order to test the feasibility of this algorithm, the dataset is divided into training dataset and test dataset, and 50 images are selected for testing.

The detection and segmentation results of concrete surface cracks by the three models are shown in Figure 17. The main differences in the image processing results of each identification method are shown in the red box. From the comparison of evaluation indicators and the image effect of detection, the Segnet image segmentation results are relatively rough, and there are cases of false detection in the crack region. In contrast, the segmentation effect of the U-net model and improved U-net model has been improved. Segnet does not make full use of the features extracted by the network coding part. It gradually restores the feature map to the original image size through simple upsampling and pooling index operations but ignores the connection between pixel positioning and classification, so the segmentation results are more coarse-grained than the other two models. U-net fuses low-level features with high-level features through jump connections to achieve finer segmentation results. Compared with the U-net model, the segmentation results of the improved U-net model in this article are most similar to the labeled images, with lower leak detection rate and false detection rate, higher segmentation precision, and shorter segmentation time for single images. This is because the improved U-net model not only has a jump-connected structure, but also uses the Inception module and ASPP module to process the feature map before upsampling, which expands the receptive field of the convolutional layer, so that the image retains more information during the convolutional process and has a powerful ability to identify the crack region. 2b1af7f3a8