To determine both the size of a satellite antenna and the thermal deformation of its surface shape, a novel high-accuracy close-range photogrammetric technique is used in this study. The method is also applied to assess the performance of the antenna in orbit. The measurement principle and solution method of close-range photogrammetry were thoroughly investigated, and a detailed measurement test scheme was developed. A thermal deformation measurement of the surface shape of a satellite antenna was then carried out. The results show that the measurement error using close-range photogrammetry was smaller than 0.04 mm, which meets the accuracy requirement. Thanks to the high accuracy, it was discovered that both the surface shape and the rib precision of the satellite antenna deteriorate with decreasing temperature. The accuracy of the surface shape and ribs was lowest when the temperature node was −60 °C. The maximum root mean square errors (RMSEs) reached 0.878 mm and 0.761 mm, respectively. This indicates that the surface shape deformation error of the antenna caused by high and low temperatures is relatively high. However, the requirement for the technical design index (RMSE ≤ 1 mm for the surface shape accuracy of the antenna) is still met. Furthermore, for temperature differences of 40 °C and 80 °C, the measured RMSEs for the surface shape deformation were 0.216 mm and 0.411 mm, respectively. Overall, the technical design indicators (RMSE ≤ 0.3 mm and RMSE ≤ 0.5 mm, respectively) for the surface shape deformation of the antennas are met.

Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.

Retro-reflective targets (RRTs) are important for obtaining the coordinates of the surface points of objects measured using industrial photogrammetry systems. Targeting the low measurement accuracy caused by the poor roundness of the conventional RRT, a novel RRT was designed in this study by covering a die-cut slice with round holes and high roundness on a glass bead reflective film. The roundness of this novel RRT was on average 0.03 mm higher than that of the conventional RRT. The accuracy of the novel RRT was evaluated in terms of both internal and external coincidence accuracies. The measurement repeatability accuracy of the novel RRT was improved by 25.0% compared with that of the conventional RRT. The thinner the die-cut slice thickness, the higher the repeatability in a certain range. Similarly, the plane fitting, flatness measurement, and standard length measurement accuracies were improved by 30.7%, 34.1%, and 28.1%, respectively.

To address the challenge that multi-camera industrial photogrammetric systems cannot rapidly achieve an optimal measurement network configuration during surface inspection of ultra-large mesh antennas, we developed a accuracy simulation software. This software incorporates a measurement network simulation module that enables the planning, design, and quality evaluation of the measurement network. By simulating the measurement network for a specific ultra-large antenna and incorporating actual measurement conditions, we determined the optimal network configuration. Based on the optimized network parameters, a rapid surface measurement system was constructed. The simulation results were in close agreement with the actual measurement accuracy of the system. The deviation between the simulated and measured imaging rates of the same camera was within 10%. Following a common-point transformation between the coordinate systems of the rapid surface measurement system and a single camera, the root-mean-square error was 0.4 mm. After multiple adjustment iterations, the surface deviation of the mesh antenna from its digital model was better than 0.5 mm. The optimal measurement network configuration derived from the accuracy simulation analysis software enables rapid surface inspection of space-borne ultra-large mesh antennas, thereby improving the deployment efficiency of the rapid surface measurement system and enhancing its measurement accuracy.