Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

In the application of quantitative remote sensing in water quality monitoring, the existence of mixed pixels greatly affects the accuracy of water quality parameter inversion, especially for narrow inland rivers. Improving the image spatial resolution and weakening the interference of mixed pixels in the image are some of the urgent problems to be solved in the study of water quality monitoring of medium- and small-sized inland rivers. We processed Sentinel-2 multispectral images using the super-resolution algorithm and generated a set of 10 m spatial resolution images with basically unchanged reflection characteristics. Both qualitative and quantitative evaluation results show that the super-resolution algorithm can weaken the influence of mixed pixels while maintaining spectral invariance. Before the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH₃-N, the R² was 0.61, the root mean squared error (RMSE) was 0.177 and the mean absolute percentage error (MAPE) was 29.33%; for Chemical Oxygen Demand (COD), the R² was 0.26, the RMSE was 0.756 and the MAPE was 4.62%; for Total Phosphorus (TP), the R² was 0.69, the RMSE was 0.032 and the MAPE was 30.58%. After the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH₃-N, the R² was 0.67, the RMSE was 0.161 and the MAPE was 25.88%; for COD, the R² was 0.53, the RMSE was 0.546 and the MAPE was 3.36%; for TP, the R² was 0.60, the RMSE was 0.034 and the MAPE was 24.28%. Finally, the spatial distribution of NH₃-N, COD and TP was obtained by using a machine learning model. The results showed that the application of the super-resolution algorithm can effectively improve the retrieval accuracy of NH₃-N, COD and TP, which illustrates the application potential of the super-resolution algorithm in water quality remote sensing quantitative monitoring.

Efficient semantic segmentation of large-scale 3D point clouds is a fundamental and essential capability for real-time intelligent systems, such as autonomous driving and augmented reality. The high dimension feature vector and the complex network structure are two major constraints to utilize the large-scale point cloud. This paper proposes an optimal feature description vector network (OFDV-Net) for 3D point cloud semantic segmentation. First, a multiscale point cloud feature extraction structure is constructed to generate an initial feature description vector (IFDV). Then, IFDV is selected by a feature selection unit to obtain the optimal feature description vector (OFDV). The OFDV encapsulates the best 3D features set of the points and can be used as the input of the deep neural network for training and testing. Finally, the OFDV-Net was applied to the standard public outdoor large-scale point cloud datasets Semantic3D and NPM3D, and the overall segmentation accuracy of 88.3% and 87.7% were obtained, respectively; moreover, the OFDV-Net requires less training time, which indicates that the algorithm can obtain high-precision semantic segmentation results on an outdoor large-scale point cloud while reducing model training time.

Mobile payment via QR (Quick Response) code is the main payment method in current days which faces with many potential payment risks. To tackle with the fraudulent transactions of QR code, the study presents a secure mobile payment system based on passive IoT, and a payment instrument featured by lifecycle energy supply is also designed. The experimental results show that the remote fraudulent transactions via QR code can be well detected. Besides, the properties of interaction time and success rate of the proposed mobile payment system perform well, especially in the low payment frequency. Besides, the payment instrument can gain sufficient energy to meet the demand of energy consumption. This paper is aimed at providing helpful solutions for IoT payments in the area of specially-engaged commercial units and government-issued vouchers.