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An increasing number of detection methods based on computer vision are applied to detect cracks in water conservancy infrastructure. However, most studies directly use existing feature extraction networks to extract cracks information, which are proposed for open-source datasets. As the cracks distribution and pixel features are different from these data, the extracted cracks information is incomplete. In this paper, a deep learning-based network for dam surface crack detection is proposed, which mainly addresses the semantic segmentation of cracks on the dam surface. Particularly, we design a shallow encoding network to extract features of crack images based on the statistical analysis of cracks. Further, to enhance the relevance of contextual information, we introduce an attention module into the decoding network. During the training, we use the sum of Cross-Entropy and Dice Loss as the loss function to overcome data imbalance. The quantitative information of cracks is extracted by the imaging principle after using morphological algorithms to extract the morphological features of the predicted result. We built a manual annotation dataset containing 1577 images to verify the effectiveness of the proposed method. This method achieves the state-of-the-art performance on our dataset. Specifically, the precision, recall, IoU, F1_measure, and accuracy achieve 90.81%, 81.54%, 75.23%, 85.93%, 99.76%, respectively. And the quantization error of cracks is less than 4%.

The rapid development of instant messaging changes the people's communication, collaboration and entertainment manner radically. Unfortunately, the existing instant messaging technology does not provide built-in support for security features. The security problem of instant messaging system has aroused widespread concern in both academia and industry. In this paper, we proposed a new and secure instant messaging system by using identity-based cryptosystems, which can provide strong authentication and secure communication (confidentiality, integrity and non-repudiation) for both instant messaging client to instant messaging server and instant messaging client to instant messaging client. The proposed instant messaging system is simpler and more efficient than those of existing instant messaging systems.

Underwater structure inspections are essential for infrastructure maintenance, such as hydraulic facilities, bridges, and ports. Due to the influence of turbidity, dark light, and distortion, the traditional methods cannot satisfy the requirements of on-site inspection applications. This paper proposed a methodology of the point cloud data capture in the turbid underwater environment. The method consisted of an acquisition device, a distortion correction algorithm, and a parameter optimization approach. The acquisition device was designed by composing a silt-removing module, a structured light camera module, and a clear water replacement module, which can integrate with an underwater inspection robot. The underwater multi-medium plane refraction distortion model was established through analysis, and a refraction correction algorithm was provided to correct the distortion. To obtain the maximum field of view of the point cloud, the nonlinear optimization approach was used to select the medium material and thickness. After the real experiments using the Intel RealSense sr300 depth camera, maximum measuring distance could range up to 253 mm in water, the accuracy of the point cloud of the underwater target objects was ±3.77 mm, and the maximum error was 8.76%. Compared with other methods, this method was more suitable for 3D point cloud capture in the turbidity environment.