The second-generation image intensifier adopts a Na₂KSb photocathode, whereas the third-generation image intensifier adopts a GaAs photocathode. Given that GaAs photocathodes have a higher cathode sensitivity, the performance of the third-generation image intensifier is much higher than that of the second-generation image intensifier. The super second-generation image intensifier, developed on the basis of the second-generation image intensifier, has been greatly improved in terms of cathode sensitivity, and thus, its performance has also been greatly improved. Simultaneously, the gap with the third-generation image intensifier has been significantly shortened. Super second-generation image intensifiers belong to the material technology of Na₂KSb, with low production cost and high cost performance compared with those of third-generation image intensifiers. Therefore, European image intensifier manufacturers chose the development roadmap for super second-generation image intensifiers. Super second- and third-generation image intensifier technologies have been developed in parallel for more than 30 years, and their performance has been greatly improved. The performance gap between super second- and third-generation image intensifiers is primarily reflected under conditions of extremely low illumination (<10⁻⁴ lx); the performance remains basically unchanged for levels above that. The performance of super-second-generation image intensifiers can still be improved. In terms of the gain, they can be improved by depositing a film of high secondary electron emission material on the inner wall of the microchannel plate. With respect to the signal-to-noise ratio, the grating window was introduced to improve the cathode sensitivity, thereby improving the signal-to-noise ratio. The resolution can be improved by inserting a semiconductor film at the output of the microchannel plate and adopting a high-definition fluorescent screen. Cathode sensitivity is a parameter of the photocathode components and not the overall performance parameter of the image intensifier. The influence of the cathode sensitivity on the overall performance of the image intensifier is embodied in the gain, signal-to-noise ratio, and equivalent background illumination. Different models are employed to distinguish between super second- and third-generation image intensifiers. These models give rise to different levels of performance. The performance parameters of super second- and third-generation image intensifiers are measured under the condition of a light source, but the spectral distribution in the actual application environment is not the same as that of the light source. The spectral responses of Na₂KSb and GaAs photocathodes are different. Therefore, performance parameters such as signal-to-noise ratio and resolution of the super-second-generation and third-generation image intensifiers are not comparable.

Pose estimation between LIDAR and imaging system is the prerequisite for the data fusion. Among current mainstream off-line calibration methods, common checkerboard is generally effective for 64-line and above LIDAR, but not for 16-line LIDAR due to its sparse data and will lead to large error. Furthermore, when involving calibration of infrared imaging system, specially-made checkerboard is needed to produce difference of emissivity. Aiming at the problem of less information provided by sparse LIDARs, we propose a new calibration method that can jointly calibrate LIDAR and visible/infrared imaging systems. A novel diamond-shaped nine-hole calibration board is designed, and a geometric constraint loss function is proposed to optimize the coordinates of feature points. Finally, the infrared and visible light imaging systems are used respectively, to calibrate with 16-line LIDAR. Good results are achieved and show that, all the average reprojection error is within 3 pixels. The proposed method can also be used in calibration of multi-band imaging systems that include sparse LIDAR, visible imaging system and infrared imaging system.

The operating range parameter of an infrared system is an important index for characterizing its imaging performance. At present, there are many methods to calculate the operating distance of infrared systems, but they all have their own applicability and limitations. It is necessary to consider the influence of various factors on the operating distance under different conditions. In this study, based on the detection energy, noise equivalent temperature difference (NETD), and contrast, we found limitations under different conditions. When the observation target was a human, the calculated operating distance based on the NETD model was 8.74 km, which is closer to field experimental data. When the observation target was an aircraft, the calculated operating distance based on the energy model was 32.04 km, which is also closer to field experimental data. These results show that, according to the different characteristics of the target, selecting the appropriate calculation method for the operating distance is helpful in improving the accuracy of the system operating distance estimation.

Helmet night vision systems are developed from single-band to multi-band image fusion. In this study, we analyzed the technical program and image registration accuracy based on low-light-level helmet observation and a hanging infrared night vision device. Optical simulation analysis was also conducted. First, we analyzed the working mode of the combination of hanging infrared night vision and low-light-level helmet, as well as the design scheme of image rotation and circular field of view. Second, according to the design index of hanging infrared night vision, optical simulation of an infrared lens and projection lens was carried out. Third, the image registration accuracy was analyzed from three viewpoints: suspension accuracy, optical axis consistency, and distortion. Finally, according to the simulation results and image registration accuracy analysis, a technical scheme based on low-light-level helmet observation and suspended infrared night vision is feasible and can achieve the targeted effect.

Pilots' night vision goggles will play an increasingly important role in future night air combats. However, wearing night vision goggles will also lead to many safety and ergonomic problems. To ensure the flight safety of pilots wearing night vision goggles and to improve night vision combat effectiveness, it is important to enhance the ergonomics of pilots' night vision goggles. This study analyzes the typical ergonomic problems of pilots when they use night vision goggles and provides suggestions for efficiency improvement from three viewpoints: performance enhancement of night vision goggles, personnel training, and use of environment and opportunity. We provide a feasible solution to improve the safety, efficiency, and comfort of pilots wearing night vision goggles.

To address the problems of loss of detailed information and blurred edges in the fusion of infrared and visible images, an infrared and visible image fusion method through the VGGNet19 network in the transform domain is proposed. Firstly, in order to extract more accurate basic and detailed data from the source images during the decomposition process, the source images are decomposed using a multi-scale guided filter with edge-preserving smoothing function into a base layer and multiple detailed layers. Then, the Laplacian energy with the characteristics of retaining the main energy information is used to fuse the basic layer to obtain the basic fusion map. Subsequently, to prevent the fusion result from losing some detailed edge information, the VGGNet19 network is used to extract the features of the detail layers, L1 regularization, upsampling and final weighted average, thus the fused detail. Finally, the final fusion is obtained by adding two fusion graphs. The experimental results show that the method proposed can better extract the edge and detailed information in the source images, and achieve better results in terms of both subjective and objective evaluation indicators.

To achieve high-precision measurements under the operating conditions of optoelectronic tracking systems and satisfy high-precision target matching in complex environments, in this study we adopted the average normalized cross-correlation algorithm. To improve the matching speed and real-time tracking, the computational complexity was simplified by using the sum table method to correlate the sum of images, squares, and the correlation of images. The wavelet pyramid method was used as the search strategy, and the center of the template was used as the reference point for cross-shaped search. A termination threshold was introduced, which reduced the number of mismatched points to increase the search speed. To verify the effectiveness of the algorithm, an optoelectronic tracking system was placed on a two-dimensional turntable in an experiment that used the algorithm to track a target. The experimental results show that the missed target was controlled within 3 pixels. The proposed algorithm can realize high-precision and stable tracking in optoelectronic tracking systems.

To overcome the defects of existing infrared image enhancement methods, such as under-enhancement, over-enhancement, and low contrast, an infrared image enhancement method based on adaptive bilateral filtering and directional gradient is proposed. The bilateral filter was improved, and its weighting coefficient is now adaptive to smooth and detailed regions. The improved bilateral filter is used as the central surround function of Retinex to decompose the infrared image into a base layer and a detail layer. Using improved platform histogram equalization, the base layer image is enhanced, and a directional gradient operator is proposed to extract the gradient image of the detail layer image to perform nonlinear adaptive edge enhancement on the detail-layer image. Experimental results show that, compared with existing methods, the proposed method can improve the brightness and contrast of infrared images more effectively. In addition, the visual effect of enhanced images using this method is better.

Aiming at the problems of high algorithmic complexity and low detection accuracy caused by overlapping occlusions in abnormal crowd behavior detection, this paper proposes an algorithm for crowd abnormal behavior detection based on an improved single-shot multi-box detector(SSD). First, the lightweight network MobileNet v2 was used to replace the original feature extraction network VGG-16, and a convolutional layer was constructed by a deformable convolution module to enhance the receptive field. Feature enhancement was performed by integrating the position information into the channel attention, which can capture long-range dependencies between spatial locations, allowing for better handling of overlapping occlusions. The experimental results show that the proposed algorithm has a good detection effect on abnormal crowd behavior.

Of all the image layered filters, guided filter has been widely studied and applied in the field of infrared image detail enhancement because of its good edge preserving effect and low computational complexity. However, traditional fixed regularization parameter ε of the guide filter cannot achieve good filtering layering effect in all scenarios. Therefore, an adaptive algorithm of parameter ε based on local variance is proposed in this paper to improve the adaptability of the guide filter in all scenarios. In addition, an improved detail layer adaptive enhancement algorithm based on noise mask function is proposed by using the adaptive parameter ε value, which can effectively suppress the noise level and improve the detail enhancement ability of the algorithm in different scenes.

The particle removal efficiency (PRE) of single-wafer substrates using dual-fluid spray-cleaning technology was investigated. The ratio displacement-diameter(H/D), which is dimensionless, is introduced to discuss the effect of PRE on a single-wafer surface. In addition, the effects of spray time and nozzle injection pressure on PRE are discussed. The results show that increasing the spray time and nozzle injection pressure can increase PRE. The highest PRE occurred when the displacement-diameter ratio was close to 1. When the ratio was less than 1, the PRE increased with an increase in the displacement–diameter ratio. When the ratio was greater than 1, the partial area of the wafer surface was not washed, and the PRE decreased rapidly with an increase in the ratio. The dual-fluid spray-cleaning method can achieve more than 99% PRE for particle sizes between 0.2 μm and 0.3 μm and more than 96% PRE for particle sizes between 0.1 μm and 0.5 μm.

The Lamb-wave-based damage location method has been widely used for health monitoring of composite structures. However, it is easily interfered by external factors because its service environment is complex and changeable. To determine the influence of the temperature field on the propagation of Lamb waves in composite structures, in this study we first investigated the propagation characteristics of ultrasonic guided waves on glass fiber laminates under a temperature field using an infrared thermal imager. Subsequently, a corrected model was established using the extracted amplitude attenuation and phase delay errors. Consequently, a modified multiple signal classification(2D-MUSIC) algorithm-based damage imaging method is proposed for composite structures. The experimental results on glass fiber composite laminates show that the proposed method can effectively improve the resolution and accuracy of the original algorithm under a variable temperature field.

This paper introduces a thermal fault diagnosis method called multi-scale guided filtering and decision fusion. The proposed method combines multiscale guided filtering and decision-fusion techniques for fault diagnosis. It comprises three main steps. First, the Mahalanobis distance between the fault area and background is estimated, and initial thermal fault diagnosis results are generated. The initial diagnosis result is then filtered using guided filtering with various parameters, and several filtering feature maps are generated. Different filtering feature maps contain complementary spatial-structure information. Finally, a principal component analysis algorithm fuses these filtering feature maps to capture their spatial structure information and thermal information in filtering feature maps. Experimental results show that the proposed diagnosis method has a better detection performance than the current state-of-the-art detectors.

Infrared thermal image processing is an effective method for detecting defects in electrical equipment. Aiming at the problem of electrical equipment segmentation in infrared thermal images with a complex background, in this study we propose a deep residual UNet network for infrared thermal image segmentation. Using a deep residual network to replace VGG16 to perform feature extraction and coding for the UNet network, a deep residual series UNET network was constructed to segment electrical equipment. To validate the effectiveness of the Res-UNet network, infrared images, including current transformers and circuit breakers, were used to test the segmentation results and were compared with the traditional UNet and Deeplabv3+ networks. The networks were tested using 876 images. The experimental results show that RES18-UNET can accurately segment electrical equipment; the segmentation precision of current transformers and circuit breakers is greater than 93%, and the mean intersection over union (MIoU) is greater than 89%. Our method obtains more accurate segmentation results than UNet and Deeplabv3+, setting the basis for intelligent diagnosis of electrical faults.

A method for defect detection and area calculation of exterior windows of buildings is proposed by combining infrared thermal imaging technology and image processing technology. Using equipment for detection of building exterior window defects, the differential-pressure method was utilized to detect the air penetration of an exterior window, and the defective area of the air penetration of this window was calculated. Infrared images of the exterior window of the building collected by an infrared thermal imager were subjected to image preprocessing, exterior window defect detection, and area calculation after inspection. Then, an infrared-image detection model of exterior window defects was established. The results show that preprocessing can make use of the weighted average method for grayscale processing, the median filter for noise reduction, image sharpening, and histogram equalization for image enhancement processing. The outcome of the aforementioned approaches is evident. The detection of the pretreatment infrared image, which is obtained using the Roberts algorithm, minimizes the difference between the test and experimental values. This makes the detection information closer to the actual position of the defect. A primary assessment of the airtightness performance level of exterior windows can be achieved by comparing the results provided by the proposed infrared image processing technology with airtightness on-site tests.