The emergence of intelligent infrared focal plane detectors relies on advanced infrared materials, device technology and process level as well as the rapid development of integrated circuit technology and process, and it is able to adaptively adjust the working state of the detector for different scenarios, and partial signal processing functions can be realized on the detector. The emergence of intelligent infrared detectors has greatly improved the flexibility of infrared systems, while providing more accurate detection capabilities for increasingly complex detection scenarios. This paper takes the background requirements of intelligent infrared detector applications as a starting point, clarifies the development of intelligent detector necessity, and introduces the development status and future trends of intelligent infrared detectors are introduced in the end.

At present, 2 m thulium-doped pulsed fiber laser can achieve the highest energy output in the order of millijoule scale, which is of great significance for medical, materials, communications and other fields. In this paper, the main advances in the research of high energy thulium-doped fiber laser system in recent years are introduced, and the technical types and influencing factors of high energy thulium-doped fiber laser are discussed as well. On this basis, the research prospect of large energy thulium-doped fiber laser is prospected.

To address the problem of low accuracy of point cloud classification and segmentation in the process of multi-target recognition, a point cloud classification and segmentation method DRPT (Double randomness Point Transformer) based on the improved Transformer model is proposed in this paper. The approach creates new point embeddings in the convolutional projection layer of the Transformer model and uses local dynamic processing of local neighborhoods to continuously add global feature attributes in the data feature vector, thus improving the accuracy of point cloud classification and segmentation in multi-target recognition. Standard benchmark datasets (ModelNet40, ShapeNet partial segmentation and SemanticKITTI scene semantic segmentation datasets) are used in the experiments to validate the performance of the model. The experimental results show that the pIoU value of the DRPT model is 85.9%, which is 3.5% higher than other models on average, and effectively improves the accuracy of point cloud classification and segmentation during multi-target recognition detection, which is an effective support for the development of intelligent network technology.

3D point clouds are becoming increasingly popular in various 3D object representations, where point-based methods have shown good performance on a variety of datasets. In response to PointNet++ only focuses on the information of the points themselves and not the information of the neighboring points, while it uses max-pooling to aggregate local information, resulting in the loss of sub-maximal information. As a result, the Con-PointNet++ network is proposed to make full use of the enhanced Local Information Module to focus on the information of neighboring points and thus enhance the local information feature extraction. Then, the fusion pooling module under the local attention mechanism is used to fuse the max-pooling and attention pooling feature information to obtain richer local feature information. The proposed method evaluates the model semantic segmentation ability on Area~~5 of indoor dataset S3DIS, with mIoU of 55.2%; and the model classification effect on dataset ModelNet40, with OA of 91.2%. Compared with other methods, the performance of the proposed model is improved, which further demonstrates the effectiveness of the proposed method.

In order to explore the effects of physical parameters such as laser overlapping rate and average laser power on the temperature distribution and ablation depth of Q345 steel surface oxide layer during laser cleaning, this paper carries out a simulation study of the laser cleaning process for the oxide layer on the surface of Q345 steel, which is commonly used in steel industry engineering. Firstly, the physicochemical properties of the oxide layer on the surface of Q345 steel are analyzed, and the laser cleaning layered model of the oxide and matrix is established. Next, with the aid of COMSOL Multiphysics software, the Q345 steel laser cleaning model is meshed and transient study is conducted, and experimental verification of the effects of laser parameters on the cleaning temperature field and depth of cleaning are obtained. The results show that the spot overlapping rate has little effect on the temperature field, but with the increase of the overlapping rate, the ablation depth is deeper. And the greater the average laser power, the higher the peak temperature field and the deeper the ablation depth significantly. The research results can provide a theoretical reference for the process study of laser cleaning of oxide layers.

The laser beam is affected by atmospheric turbulence during propagation, causing distortion of the light spot, affecting beam quality and in real life the turbulence varies with time. In this paper, a turbulent phase screen model based on the Fourier transform spectral inversion method is proposed to address above problem, and simulation research of laser propagation in dynamic atmospheric turbulence of different intensities is conducted by obtaining a dynamic phase screen based on the frozen-flow method. The simulation results show that for the same laser beam, the spot distortion increases with the increase of atmospheric turbulence intensity at the same time, and the received power density decreases overall with increasing undulations.

SLAM (Simultaneous Localization and Mapping) synchronous positioning and map construction is the key technology of intelligent perception of mobile robots. However, most of the existing SLAM methods are implemented in stationary environments, and when there are frequently moving obstacles in the environment, SLAM mapping will produce motion distortion, resulting in the robot being unable to accurately locate and navigate. Meanwhile, there are a large number of redundant 3D data points in the 3D point cloud data obtained by 3D scanning equipment such as LiDAR, and excessive redundant data not only wastes a large amount of storage space, but also affects the real-time performance of various point cloud processing algorithms. To address the above problems, a laser SLAM motion distortion removal method and a curvature-based point cloud data classification simplification framework is proposed in this paper. It optimizes SLAM motion distortion by laser interpolation, simplifying the classification of optimized point cloud data. It can improve the accuracy of SLAM mapping, and also well eliminate the redundant data points in the 3D point cloud data with unclear features, greatly improving the efficiency of computer operation.

In this paper, a method of recognizing the trunk point cloud of natural forest trees in the mining area using a hand-held laser scanner is proposed in order to quantitatively describe the three-dimensional structure information of natural forest trees in complex environment of the mining area. The natural forest in the mining area is scanned with a hand-held laser scanner. On the basis of filtering the ground points, the multi-scale verticality feature is introduced to identify the trunk of the natural forest in the mining area, and the identified trunk is further extracted by the octree leaf node clustering method. Experiments are carried out with measured data from the mining area, and the results show that the proposed method can realize the identification of the trunk of natural forest in the mining area, and has certain reference value for quantitative description of the three-dimensional structure information of natural forest trees in complex environment, and for clarifying the vegetation growth status in the mining area.

In this paper, a double-temperature heat transfer model for the gear material 18Cr2Ni4WA is established in order to analyze the composition and morphological influence of femtosecond laser ablation surface of gear tooth surface. The numerical simulation of femtosecond laser ablation surface is carried out using backward finite difference method to study the machining process of femtosecond laser ablation surface, and the influence law of energy density on recast layer and heat-affected layer is analyzed. The results show that the energy density increases from 1.73 J/cm2 to 4.33 J/cm2, the thickness of recasting layer increases from 0.68 m to 1.02 m, and the thickness of heat affected layer increases from 0.96 m to 1.35 m. Aiming at the control of recast layer, a secondary machining of opposite gear tooth surface is implemented, and the experimental results show that when the energy density of 1.73J/cm2 is used for secondary machining of the tooth surface, there is almost no recast residue; and the average roughness of the tooth surface decreases from 0.365 m to 0.185 m, which effectively improves the machining quality of the tooth surface gear, and provides a useful reference for improving the precision of femtosecond laser ablation surface gear.

The electrical properties of indium antimonide crystal material are a key factor affecting the performance of the final infrared detector. Impurities within the material as well as point defects, especially vacancy defects, can greatly affect the electrical properties of materials, and sometimes even lead to material inversion. In this paper, the vacancy defects in indium antimonide crystal materials are investigated using positron annihilation spectroscopy and the positron annihilation lifetimes of different crystal growth pull rates and conductive types of crystal growth rate are also analyzed. The results show that its internal mainly VIn type vacancy defects and within a certain range of pulling rate, the positron annihilation lifetime basically unchanged, in addition to the vacancy defects are not the main cause of the N-type indium antimonide crystal material conductivity type inversion.

As the key high-temperature component of aerospace vehicles, aero-engine contributes to the infrared radiation intensity of the entire aircraft, of which the mid-wavelength band of 3~5 m and the long-wavelength band of 8~14 m are the main radiation wave bands. Considering the significance of the infrared radiation intensity measurement and analysis, and based on the requirements of GJB-241 for infrared radiation characteristic test and identification, the establishment of a set of infrared radiation characteristics test methods taking into account the environment, meteorology, test equipment and other factors that can be applied to aero-engine racks and an engine test. The test results show that the proposed test method can meet the infrared radiation test requirements of this engine, obtain the typical infrared spectrum curves of the engine at different wavelengths, accumulate the relevant test experience of the model, and lay a certain foundation for the subsequent research.

With the technological upgrades of modern warfare, there is a growing need for faster, farther and more accurate target detection in the field of airborne infrared detection. In this paper, an improved target detection algorithm based on YOLOv7 is proposed to meet the high-precision and high-frame-rate detection of infrared dim dim targets in airborne environment. Firstly, based on the YOLOv7 target detection algorithm, the network structure is modified and the number of convolutional layers is deepened to extract more features of small target information. Moreover, the attention mechanism is introduced into the feature layer obtained by the backbone network to improve the perception ability of the neural network to perceive the small targets and increase the weight share of the region where the small targets are located. Finally, the EIOU loss function is used to replace the CIOU loss function, which improves the convergence speed and positioning accuracy. The experimental results show that compared with the original algorithm YOLOv7, the improved algorithm can reach 98.49% mAP with minimal loss of frame rate, which is 1.24% higher than the original algorithm, and it helps to improve the detection accuracy of airborne infrared dim small targets.

The purpose of this paper is to study the temperature measurement method of narrow band infrared camera. Firstly, starting from the field of image processing, an infrared thermal imager and a standard radiation source blackbody is used to collect blackbody infrared images at different temperatures at a certain ambient temperature. Then, the gray value of the blackbody image is calculated through mathematical modeling software and the correlation between the gray value and temperature is explored. Finally, the blackbody calibration curve is constructed based on the least squares method and interpolation fitting idea and the verification temperature is derived from the resulting calibration curve and the existing gray value. The validation results show that the temperature measurement accuracy has been improved, and the error is within 0.49 ℃.

In this paper, the laser irradiation indication and tracking detection of laser guided munitions for naval guns under typical conditions are simulated and verified in order to improve the combat efficiency of naval gun weapon system. Firstly, aiming at the use scenarios of laser precision guided munitions in naval gun weapon systems, a laser irradiation indication and ammunition detection and tracking model is established. Then, a laser guidance light zone simulation calculation software is compiled, and the synergy method, indication and detection capabilities between laser precision guided munitions and irradiators under different meteorological visibility conditions, different attack targets, ship's own irradiation indication, UAV on-board irradiation indication and other conditions are studied, and simulation calculations are carried out finally. The results show that the energy density of the reflected laser gradually decreases with the increase of angle c, and the larger the laser incidence angle z, the greater the energy density of the reflected laser. The laser light guidance zone produced by the two irradiation methods is relatively similar. UAV laser guidance methods and ship laser guidance methods have their own advantages and disadvantages, and can be applied to different scenarios.

Optical radio-frequency transmission technology has a wide range of applications in ground-based passive detection, distributed array synthetic aperture, and space detection, and many other areas, used to achieve signal interconnection and signal coherence between different sub-arrays. In this paper, a composite microwave photonic time-frequency transmission technology is proposed in this paper to address the problems of low phase stability, large time delay variation, and susceptibility to environmental influences in traditional optical Radio over fiber technology. By combining passive and active time-frequency transmission technology, the fiber distribution of the local oscillator point frequency signal and the fiber return transmission of the intermediate frequency broadband signal are achieved respectively, and the technical advantages of the two are combined to achieve the purpose of the system's high stability of the phase and broadband signal transmission. This system can realize the time-frequency stable phase transmission of the local oscillator signal and the intermediate frequency signal at the center end and the remote end. Through comparative experiments and comprehensive tests, the stable phase transmission of 1.6 GHz local oscillator and (1.6±0.5) GHz intermediate frequency signal is realized, and the transmission distance is 5 km. After environmental test verification at -40～70 ℃, the phase fluctuation in the upstream and downstream microwave photonics link within the temperature change range is less than ±1.5°.

In this paper, a displacement error detection scheme is proposed in view of the inability to detect the displacement error between the optical axis and the mechanical axis of the Pechan Prism in a parallel optical path. A consistency detection system for the optical axis of the Pechan Prism is designed, including the detection optical path and the control optical path. The total length of these two optical paths is equal, and the MTF is close to the diffraction limit. Then, a detection optical path is built based on the designed detection system and the Pechan Prism is mounted with a diameter of 25mm. The angle error and displacement error between the prism optical axis and the mechanical axis are detected and adjusted in both the traditional parallel optical path and the detection optical path designed in this paper. Finally, the consistency accuracy of the output optical axis and the incident optical axis of the Pechan Prism is measured to be ±48″. The mounted Pechan Prism and rotating axis system are installed and adjusted in the formal system, and the consistency accuracy of the optical axis is measured to be ± 0.8 pixels, reaching pixel level accuracy. The detection method proposed in this article improves the efficiency and accuracy of installing and adjusting the optical axis of the Pechan Prism in the system.

Traditional scalar light fields have spatially uniform polarization states, whereas vector light fields have spatially varying polarisation states compared to scalar light fields. The spatial variation of the polarization characteristics enables the vector light field to have greater control freedom with potential applications in the field of focal field design, optical micromanipulation, micromachining, and quantum information. In this paper, based on the Richards-Wolf vector diffraction theory, the effects of variable aperture as well as lens numerical aperture on the focusing characteristics of a double-ring-shaped azimuthally polarized beam are investigated based on the Richards-Wolf vector diffraction theory. The results clearly show that by changing the aperture size and lens numerical aperture, it is possible to change not only the focal spot intensity and the focal spot size, but also the length of the dark channel in the focal field, resulting in longer dark channels. The resulting variable focal field dark channel can be used in the future for optical micromanipulation and microprocessing applications.

Accurate infrared small and weak target detection is the key to real-time monitoring, tracking, and guidance. Infrared weak and small targets have problems of high detection difficulty, high false detection, and serious missed detection. In this paper, an ultra-lightweight infrared dim small target detection algorithm SL-YOLO is proposed to improve the real-time performance and detection accuracy of infrared dim small target detection algorithms. Firstly, the downsampling scheme is redesigned to adjust the network architecture for the infrared image feature information to solve the problem of feature gradient reduction and feature disappearance for infrared weak targets. Then, a network model pruning algorithm is designed to integrate pruning algorithm with network structure, removing redundant parameters, and improving detection speeds. Finally, the SIoU Varifocal loss function is designed to equalise the positive and negative samples with overlapping losses while weighting the positive samples to solve the problem of background interference. The experimental results show that the detection accuracy is improved to 96.4% and 98.1% under the SIRST and IDSAT datasets, respectively. The model volume and computational complexity can be compressed to 190 kB and 0.9 GFLOPs, and the inference speed is reduced to less than 3 ms. Comparing with the mainstream algorithms, the improved algorithm has achieved good results in terms of detection accuracy, model volume; computational complexity. It can meet the real-time detection requirements.

This research is oriented towards the practical problems in the field of infrared air-to-air early warning and detection, and carries out the design of the target detection algorithm for high-resolution infrared images and verification of engineering applications. In the paper, target detection algorithm based on median filter, convolutional filtering kernel morphological filtering, etc. are studied and designed, and the hardware transplant based on FPGA platform is verified and tested. Experiments show that the method adopted in this paper can achieve the target detection function of high-resolution infrared image, and improve the operation speed of the algorithm on the basis of FPGA hardware. The algorithm meets the requirements of real-time computing on the FPGA hardware platform.

In this paper, a binocular heterogeneous imaging system with simple structure and high real-time characteristics is built by combining infrared camera and RGB camera in order to solve the problems of unsatisfactory imaging effect of infrared image and visible image in night vision environment. Firstly, the infrared and visible images are acquired in real time using a dual-threaded approach, and an entropy-based adaptive h-solving method is proposed for Non-Local Mean filtering, which is capable of better eliminating the noise of infrared images. Then, the feature points of infrared and visible images are matched, and a slope consistency-based method is introduced for image alignment, and finally images are fused by combining the Two-scale Image Fusion (TIF) algorithm using improved base image fusion rules and multiple fusion algorithms to obtain the Infrared Radiation and RGB (IR-RGB) fusion image with obvious targets, rich information and true to color. The TIF algorithm can efficiently and rapidly fuse infrared images with visible images, preserving the true color information characteristics of visible images for the surrounding environment while retaining the temperature extraction characteristics of infrared images. According to the data analysis, the entropy value of the IR-RGB images of the TIF algorithm is improved by about 5.14%; their edge intensity all reach a maximum of 39.991, 22.433, 52.880. Compared with the common pixel-level fusion methods, the speed of the proposed method is improved by an order of magnitude of 10 times. The research is of great importance in practical applications such as target identification and monitoring by means of real-time imaging of infrared and visible light in a night vision environment.

A non-contact FBG displacement self-calibration device is studied using the non-contact force transmission characteristics of magnets, with FBGs as the core component, a cantilever I-beam and permanent magnets as auxiliary components to solve the problem that the fiber Bragg grating displacement sensors are susceptible to temperature and vibration and other external factors leading to poor measurement repeatability. Self-calibration of position information is performed using the maximum magnetic force applied to the I-beam when the position of the magnet at the moving end coincides with that of the magnet at the position calibration end, which in turn improves the measurement repeatability and measurement accuracy of the FBG displacement sensor. By mountings self-calibration device on the displacement sensor of the tape measure structure, the sensing characteristics of the device are studied experimentally and the results show that the average root-mean-square error before calibration is 5.838mm, and the average root-mean-square error after calibration is 0.953mm over the measurement range of 0~360 mm. The self-calibration device used in this paper greatly improves the measurement repeatability of FBG displacement sensor, which provides a new method for the optimization of environmental adaptation of displacement sensors.

In this paper, a fiber optic sensing system based on fiber optic Sagnac interferometer is proposed for ultrasonic detection of multi-point partial discharge. The detection system consists of a light source, a single-mode fiber delay line, a fiber coupler, a phase modulator, a polarization controller and a fiber Sagnac interferometer with a contrast ratio of 1. Firstly, the principle of ultrasonic detection of the sensor system is introduced, and then an ultrasonic experimental platform for simulating partial discharge is built. Experiments on the propagation of partial discharge ultrasound in steel plates and transformer insulating oil using surface waves excited by irradiation of steel plates with a Nd∶YAG laser and a high-frequency acoustic source. The experimental results prove the effectiveness of using this system for partial discharge ultrasonic detection, with a signal-to-noise ratio of more than 40dB in the frequency range of 100 kHz-200 kHz. At the same time, the experiments validate the multi-point detection ability of the Sagnac sensor for partial discharge ultrasonic. The optical fiber ultrasonic detection system has a simple structure, high frequency response and low cost, and has great potential in the fields of partial discharge.