Non-line-of-sight imaging is a rapidly developing imaging technology aimed at practical applications. The imaging mode based on a SPAD array has advantages such as staring imaging and lightweight equipment compared with others. This study analyzes a non-line-of-sight imaging method based on a time-gated SPAD array, focusing on the scattering characteristics of the BRDF of the relays in the time-gated SPAD array mode of non-line-of-sight imaging and its impact on imaging. The Cook-Torrance model was selected to model and analyze the BRDF scattering characteristics of typical building materials that serve as relays. The BRDF data of three typical building materials, namely black glossy tiles, white matte tiles, and black protective shields, were measured. The RANSAC algorithm was used to eliminate measurement outliers, and a genetic algorithm was employed to obtain the characteristic parameters of the model. This study validates the effectiveness of describing the BRDF scattering characteristics of materials using characteristic parameters, which can lay an important foundation for further research on time-gated SPAD-array-based non-line-of-sight imaging.

The axis alignment test system is an important instrument for measuring the parallelism of optical axes by applying the method of large aperture parallel light tubes, with a focal length of 4800 mm and an aperture of 600 mm. During the test process, due to the long focal length and large aperture, the small deformation of each component caused by the change of the ambient temperature will affect the imaging quality of the system and the test accuracy. Therefore, an opto-mechanical thermal integration analysis of the system is required. The finite element analysis model of the shaft alignment test system was established, the thermal deformation of the system under steady state temperature field and temperature gradient field was analyzed, the Zernike polynomials were used to fit the surface shape of the mirror after thermal deformation, and the fitting coefficients were imported into the optical design software to get the effects on the shaft alignment test system under different temperature changes, and the accuracy of the simulation results was verified by experiments. The results show that: under the steady state temperature field, within the temperature range required by the design parameters, the wave aberration of the optical system is less than /10 (=632.8 nm), and the accuracy of the optical axis consistency detection meets 0.02mrad; the temperature gradient field has a greater impact on the system, and it is necessary to control the temperature difference between the inside and the outside of the system to within ±3℃.

Considering the influence of temperature and pressure on the measurement of the partial pressure of oxygen using TDLAS technology, a temperature and pressure compensation algorithm for laser oxygen partial pressure sensors is proposed for eliminating these effects. The laser oxygen partial pressure sensor was constructed using an analog circuit, and the measured second-harmonic peak, temperature, and pressure values were uploaded to the master computer through an electrical connector, which performed temperature and pressure compensation using the BP neural network temperature and a pressure compensation algorithm to obtain accurate oxygen partial pressure values. The experimental results show that the algorithm can achieve compensation for the measured partial pressure of oxygen, and that the error of the partial pressure of the oxygen measurement is less than ±1 kPa, which meets the requirements of aerospace, aviation and other fields, and has a desirable application prospect.

The refrigeration controller is an important component of an infrared remote sensing camera, and its temperature control accuracy directly affects the imaging quality of the camera. With increasing requirements for temperature control performance indicators and demand for refrigeration of camera components, the development of new multifunctional, high-performance, low-weight, and low-cost refrigeration controllers has become increasingly urgent. In response to these issues, a centralized multi-load infrared remote sensing camera refrigeration controller that adopts the method of circuit module reuse, temperature measurement, and control separation to achieve multi roads control was designed in this study. In terms of hardware, modular reuse and design miniaturization are carried out on the basis of traditional refrigeration controllers, achieving a centralized hardware product composed of a "single power supply, single master control, and multiple drivers." When a temperature measurement is proposed, the control separation method of "controller + temperature measurement box" is enacted and temperature data is collected nearby to ensure the temperature measurement accuracy and improve the temperature control accuracy. In terms of software, the design incorporates a complete software architecture, measurement and control temperature matching, independent design for multi load drives, and temperature data reliability, in conjunction with hardware solutions. Based on this design scheme, the experimental data of the "Drive and Road Three" refrigeration controller shows that the design meets the functional performance requirements of temperature matching, independent driving, and precise temperature control. At the same time, the cooling and temperature control accuracy of ± 8 mK fully meets the requirements of high-quality imaging, which makes this scheme feasible.

Organic light-emitting devices (OLEDs) have attracted wide attention in the display industry owing to their excellent characteristics, such as active luminescence, fast response time, wide viewing angle, and compatibility with flexible electronics. Nevertheless, organic luminescent materials are vulnerable to environmental moisture and oxygen, and they fail once they come in contact with water and oxygen. The formation of black spots is one of the main factors affecting the product yields of these devices. Relevant studies have proposed several mechanisms for the formation of black spots; however, there is no specific model. In addition, current models of the formation of black spots are relatively vague and unpredictable. To reduce and prevent the formation of black spots, this study summarizes the causes of black spot formation, discusses the formation mechanism of black spots, and provides preventive measures.

Multispectral detection technology differs from traditional single broad-spectrum detection technology in that it can detect multiple spectra within the electromagnetic band separately, thereby obtaining more spatial material information. This technology plays a crucial role in complex and changing military environments such as remote sensing imaging, missile guidance, and biomedicine. This paper discusses the application of multispectral detection technology in imaging from the perspective of multispectral detection technology, and firstly introduces four common multispectral optoelectronic detection instruments and technologies; then presents the current development status of this technology in target background identification, missile guidance and early warning, and landmine identification and detection; and finally, anticipates the possible development direction of multispectral detection technology in the military field.

To solve the problems of scattered point interference and data holes in 3D target reconstruction, and to improve the 3D visual reconstruction effect, a 3D visual reconstruction algorithm based on polarization multispectral fusion was proposed. A binocular laser scanning and polarization multispectral imaging system was built, and a fusion algorithm was used to filter the point cloud by considering the characteristic region of multispectral mapping as the two-dimensional boundary of the target 3D point cloud. The precision of Gaussian and extremum sampling was tested experimentally. The mean deviation between the mapping position and the actual position was 0.59 mm and 0.93 mm, respectively. The average noise intensity in the background area was reduced from 49.5 to 13.4 after the superposition of four polarizers for noise reduction. Testing the target features with two different local curvatures determined that after optimization, over 80% of the test points had an error better than 3.05 m, with an average deviation of 1.49 m. Moreover, the 3D visual reconstruction effect of the target was improved.

Aiming at the problem of the high error rate of infrared small-target recognition and the large loss of model regression in complex backgrounds, an improved YOLOv8~~SG (Small goals) algorithm was proposed by adding a small target detection layer and introducing the SA attention mechanism and WIoU~~v3 loss function, which can fuse deeper features and have a larger receptive field. Moreover, the influence of the uneven labeling quality of the training samples was reduced, the position accuracy of the prediction box was improved, and the ability to detect small targets was enhanced. The experimental results show that the mAP of the improved algorithm increased from 0.8514 to 0.8997, and the overall loss effect of Box~~loss increased by 34.9%. The proposed algorithm has a higher feature extraction ability and higher detection accuracy for small-target detection.

Aiming at the problems of low detection accuracy and high false alarm rate of infrared weak and small targets due to violent background changes, more clutter and low signal-to-noise ratio in complex scenes, an infrared weak target detection algorithm combining image global information and local contrast is proposed. The algorithm uses the variance and signal-to-noise ratio to statistically analyze all pixels of the image, and processes the image globally to obtain a feature map, so as to adapt to the complex background with sharp gray level changes, while suppressing a large number of flat background clutter and improving the signal-to-noise ratio of the target. Aiming at the strong background edge noise and bright pixel noise mainly existing in the feature map, the weighted absolute directional mean difference (WADMD) algorithm is used to calculate the absolute average difference between the target and the background as the weighting coefficient, and the judgment threshold is used to suppress the negative contrast, and suppress the high luminance noise, and improve the significance of the target. Experiments show that compared with the comparison algorithm, the proposed algorithm can adapt to the changeable complex background, and improve the signal-to-noise ratio of the target more obviously, and have better robustness.

To comprehensively evaluate the performance of a UV image intensifier, a testing apparatus was established for measuring the signal-to-noise ratio of the UV image intensifier assembly. First, according to the working principle of the UV image intensifier and the method used to test the SNR of the low-light-level image intensifier, the incident radiation power required to measure the SNR of the UV image intensifier was calculated. Second, based on the measurement of the ultraviolet wavelength parameters, a method for testing the signal-to-noise ratio (SNR) of the UV image intensifier with a small-spot imaging detection is proposed. Subsequently, based on the detection method of small-spot imaging for determining the radiation intensity, an SNR measurement device was established for the UV image intensifier, and the measurement uncertainty of the device was analyzed. Finally, the SNR of the ultraviolet image intensifier was measured using a calibrated test device, and the measurement repeatability was high. The results showed that the uncertainty of the UV image intensifier SNR test device based on the detection method of small-spot imaging with the determined radiation intensity was 13.3%, the SNR of the UV image intensifier was 15.3, and the repeatability deviation of the SNR was 2.86%. This satisfies the measurement requirements of UV image intensifiers with a high SNR in ultraviolet alarms and other application scenarios.

To address the challenges of low accuracy, low efficiency, and significant time and labor costs of the current defect detection process for hydropower station equipment, a defect detection algorithm for hydropower station equipment based on the attention mechanism of infrared temperature measurement data is proposed. First, a new attention module, Coordinate Attention (CA), is introduced into the YOLOv5s feature extraction network to better capture key information in the input and improve the model performance and accuracy. Second, we replaced the ordinary convolution and C3 modules in YOLOv5 with Ghost Revolution and Bottleneck Transformer to reduce the model's parameters and computational complexity. Finally, the defect detection algorithm is combined with the PyQt5 interface to achieve visual image detection and output detection results. The experimental results show that the improved YOLOv5 model's mAP@0.5 is increased by 6.3%, and P and R are increased by 6.6% and 5.9%, respectively, meeting the detection needs of hydropower station equipment defects and achieving a higher detection accuracy.

Photovoltaic (PV) panels are an important component of photovoltaic power stations. They must be tested regularly to ensure a safe operation of the photovoltaic power station. To address the problem of small targets being difficult to detect among the complex background of aerial photovoltaic images, a defect detection method based on YOLOv7-EPAN for infrared photovoltaic panel images is proposed. First, an extended efficient network CS-ELAN module integrated with a CSWin Transformer is proposed for capturing global information effectively and suppressing background information. Second, an efficient path aggregation characteristic pyramid network (EPAN) is constructed based on CS-ELAN to enhance the information interaction between different feature layers, enrich the semantic feature information, and improve the feature expression ability. Finally, the loss function is optimized to focus the model on a prior high-quality frame and improve the positioning accuracy of small targets. The experimental results show that compared with the original YOLOv7 model, the mAP50 and mAP50:95 of the proposed method show an improvement of 6.4% and 3.3%, respectively, indicating that the proposed method can better solve the problem of missing small target defects among the complex background of aerial photovoltaic images.

To improve the accuracy of fault detection for heterogeneous insulators with complex backgrounds, an improved YOLOv7 model-based insulator fault recognition method based on heterogeneous images is proposed in this paper. An image of a heterogeneous insulator was registered and fused to highlight the location of the insulators and fault information. Then, to reduce the computational complexity and improve portability, the original backbone feature extraction network of YOLOv7 was replaced by the MOBELINET network. To reduce the problems of missing and false detections of insulators in complex backgrounds, the original loss function of YOLOv7 was changed from CIOU to FOICAL-EIOU to further improve the regression effect of the model prediction box. Finally, a deformable convolutional DCNv2 was introduced in the YOLOv7 detection head to enhance its adaptability to insulator heating fault areas of different scales. The experimental results show that the mAP value of the improved model is 96.6%, which is 9.9% higher than that of the original YOLOv7 model, the number of parameters decreases by 30.5%, and the floating-point operator decreases by 49.2%, which are 12.2% and 12.4% higher than those of the YOLOV5 and YOLOV8 target detection models, respectively. The proposed improved model could effectively detect and identify heterogeneous insulators.

Because of the demand for nondestructive testing of the bonding quality of soft foam composite structures, excitation mode research was conducted, and the pulsed infrared thermal image mode was determined suitable. A first-derivative processing method was used to form a featured image, which improved the image signal-to-noise ratio and defect resolution. Debonding defects in the specimens were effectively identified using the constructed neural network recognition software. The results revealed that the risk of false and missing detections in manual evaluation can be reduced by intelligent detection, and that the detection efficiency can be improved. The above research meets the requirements for the rapid detection of soft foam composite structures in spacecrafts.

The roll–pitch electro-optical pod mounted on an unmanned flight platform has a high velocity–height ratio. When observing the ground, a large image motion can occur during the exposure time of the photoelectric sensor, causing image blurring, and thereby, affecting the imaging quality. Simultaneously, a rapidly refreshed video scene results in the operator facing challenges in finding the target of interest. This in turn causes difficulties in tracking and capturing. Given the aforementioned issue, in this study, the information on inertial and electro-optical pods is used to calculate the angular velocity relative to the target motion and eliminate the adverse effects of carrier motion via the reverse angular motion of the equipment. Furthermore, limitations on compensation application and error analysis are provided. The relationship among target distance, compensation angular velocity, observation angle, and flight altitude is simulated. This is followed by a simulation analysis of the influence of inertial measurement error and frame angle error on the compensation angular velocity error. The actual hanging flight test shows that the electro-optical sight axis can point to any target area continuously and stably for a long time without considering the limited motion range of the equipment and provide clear and stable images as the output, without being affected by the translational motion of the carrier. This in turn provides significant convenience to the operator for observation and operation.

An infrared guidance system with high-pressure nitrogen refrigeration was introduced, and a noise reduction optimization design was conducted for a photoelectric pod with a sealed structure. To design an air return hole for a photoelectric converter refrigeration unit, fluid simulation methods were used to obtain optimized structural parameter values, system verification passes, background noise reduction, and improved system detection sensitivity. The simulation results verified the correctness and feasibility of the optimization scheme, which has engineering-guiding significance for the system design of photoelectric weapons.