We establish a theoretical model of the Doppler effect in absolute distance measurements using frequency scanning interferometry (FSI) and propose a novel FSI absolute distance measurement system. This system incorporates a basic FSI system and a laser Doppler velocimeter (LDV). The LDV results are used to correct for the Doppler effect in the absolute distance measurement signal obtained by the basic FSI system. In the measurement of a target located at 16 m, a measurement resolution of 65.5 μm is obtained, which is close to the theoretical resolution, and a standard deviation of 3.15 μm is obtained. The theoretical measurement uncertainty is 8.6 μm + 0.16 μm/m R_m (k = 2) within a distance range of 1 m to 24 m neglecting the influence of air refractive index, which has been verified with experiments.

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

We propose a frequency swept interferometry (FSI)-based absolute distance measurement method that can be used to measure a noncooperative target located at a distance of 10s of m. In this method, an external cavity laser serves as the frequency tuning laser, and a single frequency laser and two acoustic optical modulators (AOMs) are used to measure the optical path difference (OPD) variation during the frequency tuning, which can correct the Doppler effect. A phase-locked loop (PLL) is introduced to synchronize the nonlinearities between the OPD variation measurement signal and the absolute distance measurement signal, improving the signal-to-noise ratio (SNR) of the OPD variation measurement signal. The distance to a noncooperative target located at 15 m is experimentally measured using this method, and a precision of 3.43 μm is obtained.

An automatic classification method based on machine learning is proposed to distinguish between true and false laser-induced damage in large aperture optics. First, far-field light intensity distributions are calculated via numerical calculations based on both the finite-difference time-domain and the Fourier optical angle spectrum theory for Maxwell’s equations. The feature vectors are presented to describe the possible damage sites, which include true and false damage sites. Finally, a kernel-based extreme learning machine is used for automatic recognition of the true sites and false sites. The method studied in this paper achieves good recognition of false damage, which includes a variety of types, especially attachment-type false damage, which has rarely been studied before.

Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very important to ensure their normal operation. Traditional detection methods rely on manual inspection, which consumes a lot of time and labor, while deep learning methods can greatly alleviate this problem. However, previous studies have often focused on how to better detect crack defects, with the corresponding image resolution not being particularly high. In this study, targeting the scenario of real-time detection by drones, we propose an automatic detection method for dam crack targets directly on high-resolution remote sensing images. First, for high-resolution remote sensing images, we designed a sliding window processing method and proposed corresponding methods to eliminate redundant detection frames. Then, we introduced a Gaussian distribution in the loss function to calculate the similarity of predicted frames and incorporated a self-attention mechanism in the spatial pooling module to further enhance the detection performance of crack targets at various scales. Finally, we proposed a pruning-after-distillation scheme, using the compressed model as the student and the pre-compression model as the teacher and proposed a joint distillation method that allows more efficient distillation under this compression relationship between teacher and student models. Ultimately, a high-performance target detection model can be deployed in a more lightweight form for field operations such as UAV patrols. Experimental results show that our method achieves an mAP of 80.4%, with a parameter count of only 0.725 M, providing strong support for future tasks such as UAV field inspections.