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Faced with measurement conditions such as high-temperature forging, strict prohibition of surface contamination, and toxic environments, using the projection point of an optical target projector (referred to as an “optical projector”) as a photogrammetric target has become a necessary method of high-precision industrial photogrammetry. In connection with the current industrial demand, we have analyzed the principles of optical projectors and introduced their optical characteristics and advantages in the field of industrial photogrammetry. On this basis, a series of tests such as brightness, roundness, and so on were conducted to determine the basic properties of the optical projector. A set of performance test methods including inner coincidence accuracy and outer coincidence accuracy were proposed; the tests included industrial photogrammetry system measurement repeatability, surface measurement precision, and a comparison test with laser tracker. The test conditions used optical projection points as the photogrammetry targets. The test results showed that the coordinate measurement repeatability of the industrial photogrammetry system is 0.010 mm, and the surface measurement precision is 0.007 mm under the condition of a single optical projector station, with little difference between the results under the condition of pasting retro-reflective targets. In the process of the comparison test with laser tracker, the image quality of the black measurement object obtained is obviously inferior to other surfaces, so the analysis of the point projector is greatly affected by the color of the measured object and other conditions, which provides a reference for the measurement object and application range of the industrial photogrammetric system based on optical targets. The results demonstrate the applicability and reliability of using the optical projection point of an optical projector as target points for photogrammetry.

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.

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

To solve the problems of small area, slow calculation speed and low precision in the projection transformation algorithm of raster data, the idea of using Kriging interpolation approximate grid algorithm for raster data projection transformation is proposed. Through the experimental results of point-to-point transformation and Kriging interpolation approximate grid algorithm transformation under the same conditions, it can be seen that for different projection types under the same limit conditions, Kriging interpolation approximate grid algorithm can ensure that the raster data projection error is always within the given projection limit. With the same number of points, the Kriging interpolation approximate grid algorithm is faster and more efficient than the point-to-point projection algorithm. Under the same pixel condition, the Kriging interpolation approximate grid algorithm is faster, more accurate and more effective than the point-to-point projection algorithm.

A hyperspectral image classification method based on a mixed structure with a 3D multi-shortcut-link network (MSLN) was proposed for the features of few labeled samples, excess noise, and heterogeneous homogeneity of features in hyperspectral images. First, the spatial–spectral joint features of hyperspectral cube data were extracted through 3D convolution operation; then, the deep network was constructed and the 3D MSLN mixed structure was used to fuse shallow representational features and deep abstract features, while the hybrid activation function was utilized to ensure the integrity of nonlinear data. Finally, the global self-adaptive average pooling and L-softmax classifier were introduced to implement the terrain classification of hyperspectral images. The mixed structure proposed in this study could extract multi-channel features with a vast receptive field and reduce the continuous decay of shallow features while improving the utilization of representational features and enhancing the expressiveness of the deep network. The use of the dropout mechanism and L-softmax classifier endowed the learned features with a better generalization property and intraclass cohesion and interclass separation properties. Through experimental comparative analysis of six groups of datasets, the results showed that this method, compared with the existing deep-learning-based hyperspectral image classification methods, could satisfactorily address the issues of degeneration of the deep network and “the same object with distinct spectra, and distinct objects with the same spectrum.” It could also effectively improve the terrain classification accuracy of hyperspectral images, as evinced by the overall classification accuracies of all classes of terrain objects in the six groups of datasets: 97.698%, 98.851%, 99.54%, 97.961%, 97.698%, and 99.138%.