Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496   ×   496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

Regular inspections of bridge substructures are very important for evaluating bridge health, since early detection and assessment offer the best chances of bridge repair. However, the traditional inspection methods of checking defects with visual features cannot meet engineering needs sufficiently. Although deep-learning methods have recently demonstrated a remarkable improvement in image classification and recognition, there are still difficulties, such as the countless parameters and large model training sets needed by these methods. In this paper, we propose a novel crack extraction algorithm for automatic segmentation of cracks and noise using multi-layer features extracted from a fully convolutional network and a naive Bayes data fusion (NB-FCN) model. The bridge images in both the training and testing datasets are taken using an in-house designed high-precision image acquisition device, called Bridge Substructure Detection 10 (BSD-10). BSD-10 is applied to collect 7200 images from ten existing bridges under different illuminants and distances. After gathering the crack datasets, the crack and noise models of the NB-FCN are trained, respectively, with multiple iterations. Next, the skeleton and continuous boundary of a crack are recognized. Then the crack length and width are calculated using electronic distance measurement to verify the error rate of the proposed method. Compared to up-to-date machine-learning-based algorithms, i.e. the crack tree algorithm, the random structured forests algorithm, the relatively competitive convolutional neural networks algorithm, and the fusion convolutional neural network algorithm, the significant superiority of the NB-FCN algorithm in terms of recognition accuracy, computation time, and error rates is illustrated based on different types of crack images of handwriting, peel off, water stains and repair traces. The NB-FCN algorithm is verified with 7200 datasets of bridge substructures collected from 20 in-service bridges under various circumstances. In general, the recognition results show that the proposed algorithm demonstrates a remarkable performance compared to other recent algorithms.

In this study, the bridge concrete structure is taken as the research object, and the real image is used for crack identification. In structural engineering, surface cracks are the main indexes of durability and service performance of structures. Artificial visual inspection is often considered ineffective in terms of cost, safety, evaluation accuracy, and reliability. In this article, a simple, high-classification framework based on ResNeXt with postprocessing (ResNeXt+PP) model is provided to effectively identify concrete cracks. During the training phase of the method, image binarization approach is used to extract the candidate crack regions. It is difficult to automatic identify cracks from images containing actual cracks and noises, especially, shadows, stains, masses, and holesoften occur in concrete surfaces. Thereafter, classification models are constructed based on ResNeXt+PP module. Based on the new concrete surface images including cracks and noncracks, the obtained methods for crack identification are compared quantitatively and qualitatively. Besides, the five complete raw images are used to study the robustness and practicability of the method. The binary transformation process based on a binarization method of adaptive crack width is adopted to identify crack pixels in subimages. Results show that the trained improved ResNeXt+PP can automatically detect cracks and noncracks in the raw image. The obtained results that the method is superior to multiple methods and the application prospect of autonomous concrete structure driver for bridge detection robot are presented.

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Abstract. Students are misled by the traditional concepts after enrolling university, these make some students face many problems such as no goal of learning, no power, that causes students to face serious consequences that failed in courses and quit, bring serious influence to student management. In order to improve the management level of the school and help the students to get rid of the predicament, it is necessary to make an early warning. Calculate the similarity between the student and other students, and make sure the nearest neighbors based on similarity to estimate grade. Principal component analysis is an effective method to extract the main features, which is used in the process of grade warning. The method is applied to cluster students, and the result shows that it is feasible.