Ultraviolet (UV) photomultipliers are key detectors in UV warning systems and communication. The UV microchannel plate photomultiplier tube (MCP-PMT) has the merits of high sensitivity, high gain, high resolution, low noise, small volume, and vibration resistance. However, the UV photomultiplier was invented too recently, and its technical performance is weak. Thus, further development and performance studies of the UV MCP-PMT has attracted increased attention. In this paper, a UV MCP-PMT using an end window structure is discussed. MgF2 and Cs2Te are used as the light window material and photocathode material, respectively. The UV MCP-PMT can detect the "day blind" UV band from 200 to 300 nm. A high-gain double channel plate structure is adopted. The device has a multiplication ability of approximately 5×106 under low voltage, thus improving the single photon detection capability of the UV MCP-PMT. This paper gives a brief introduction of the applications of UV MCP-PMT and the development of this type of photomultiplier in China and foreign countries. The test method of UV MCP-PMT is studied. The performance of the self-developed photomultiplier is evaluated, and the obtained data are analyzed. The results show that the MCP-PMT has high cathode radiation sensitivity with good response to single photons. Compared with the same type of products abroad, this design has the advantages of high gain, high peak valley ratio, and high resolution.

Infrared earth sensors are an important attitude measurement component of satellite control subsystems. They provide the information about the satellite’s attitude relative to the pitch and roll direction of the earth radiation disk. The infrared detector is the key part of such a sensor. In this paper, we discuss research on micro-infrared thermopile detectors for space applications. Infrared thermopile technology and the technical index of the detector are introduced. The ring effect, temperature compensation, response compensation, nonuniformity correction, and blind element processing of the detector are studied. The scheme design and field programmable gate array (FPGA) design of an infrared earth sensor image processing system are described. Test results show that system’s attitude measurement deviation is less than 0.2°. Thus, FPGA-based micro-infrared thermopile detectors based on FPGA can be used for attitude measurement in low earth orbit satellites. Offering miniaturization, low power consumption, and low cost, this technology has the potential to replace traditional mechanical scanning infrared earth sensors and has broad application prospects.

The traditional exposure methods of shortwave infrared focal plane spectrometer have shortcomings in a variety of applications. This paper proposes a balanced exposure technique to eliminate these shortcomings. To improve the signal to noise ratio of the spectrometer, different spectral dimensions are given different integral times. We built an experiment system that included a shortwave infrared focal plane detector, a driver circuit, a signal acquisition and processing circuit, and PC software. The results of five different integral times (3.31 ms, 5.76 ms, 8.22 ms, 10.68ms, 13.14ms) and the contrast between them are close to the theoretical values. We conclude that this exposure technique can significantly improve the signal to noise ratio.

Object detection and tracking is an essential module in airborne optoelectronic equipment, and its performance is directly related to the accuracy of object perception. Improved Siamese network tracking algorithms have produced excellent results for various challenging datasets recently, but most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will result in tracking drift and eventually cause tracking failure. To solve these problems, this paper proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection; the algorithm uses the contour template of the target instead of the bounding box template to reduce the background clutter interference. A branch is added to the Siamese network to improve the tiny-YOLOv3 object detection network, where K-means clustering is used to find the most suitable anchor box. An expansion module layer is introduced to expand the receptive field. Therefore, our proposed model increases the anti-occlusion ability of the system and improves the object recapture probability of airborne optoelectronic equipment. The results of a simulation of benchmark test data set and a flight dataset show that the improved model is especially suitable for tracking and recapture of moving objects in complex environments; in addition, it can better adapt to deformed or occluded objects in long-term tracking, which improves the system response time and adaptability.（LSJGMS1811）。

Electric power fittings are the critical components of power transmission and transformation structures. Under the action of environmental factors, such as temperature and sandstorm, structural defects can lead to corona discharge, which can ultimately affect the safe operation of power transmission and transformation lines. Based on the working platform of a corona test, the feature extraction and evaluation of discharge spots were carried out in this study using image fusion and image processing technology. The results prove that the relationship between the spot area and discharge capacity is linear. The discharge stages of electric power fittings develop from the corona stage to a small electric arc stage, and finally, to a strong spark discharge stage. When the observation distance is 10 m, the gain is 70%, and the discharge spot areas of three stages are approximately 1000, 5000, and 10000 pixels. Based on the characteristics of the ultraviolet image, the state of electric power fittings can be divided into four states: no defect, general defect, serious defect, and emergency defect. The study results provide technical support for the safety evaluation of electric power fittings.

The multipoint calibration method provides high calibration accuracy. However, a reasonable number of calibration points are required in order to achieve the theoretical correction effect. This work proposes an algorithm for selecting multipoint legal punctuation. The algorithm uses the residual as the criterion for selecting calibration points and adaptively determines the calibration points on the focal plane response curve. The non-uniformity of the response rate of the original image collected by the detector and corrected by the proposed algorithm is 0.31%, which is about 31% less than that of the traditional uniform segmented multipoint method. In addition, the correction accuracy of the multipoint method is significantly improved.

To improve the performance of object detection and recognition in a real-world combat environment, an improved intelligent object recognition algorithm based on infrared saliency object guidance is proposed. It uses the object information in an infrared image to guide deep self-learning in vision images. The improved YOLO-V3 recognition network is based on the Darknet-53 network architecture, using DenseNet instead of the original transfer layer with lower resolution. Classification network pretraining, multiscale detection network training, and other measures are used to enhance feature propagatio n and reuse and fusion performance. Simulation results show that the proposed model can effectively improve the performance of existing object detection and recognition networks.

The detection and recognition of persons and vehicles in the nighttime environment is highly important in the fields of self-driving cars and security. This paper proposes to use images taken by a cost-effective low-resolution infrared thermal imaging camera. We optimize the faster region-based convolutional neural network according to the unique nature of the images. A multi-channel convolution layer is added to accommodate the grayscale characteristics of thermographic images. We use a global average pooling layer so that fewer images and categories are needed, and we add batch normalization layers to prevent the appearance of exploding or vanishing gradients after the network is widened. The network is trained and tested using 2000 low-resolution thermal images collected in an urban nighttime environment. The average accurate recognition rate is 71.3%, indicating that the method effectively solves the problem of detection and recognition of persons and vehicles in the nighttime environment. The stickiness value and application potential are high.

The fusion of the low-frequency subband in the non-subsampled shearlet transform (NSST) domain requires artificially obtained fusion modes; thus, the spatial continuity and contour detail information of the source image are not adequately captured. An infrared and visible image fusion algorithm based on a convolutional neural network is proposed to solve this problem. First, the Siamese convolutional neural network is used to learn the characteristics of the low-frequency subband in the NSST domain and output a feature map that measures the spatial detail information of the subbands. Then, on the basis of the feature map obtained by Gaussian filter processing, a local-similarity-based measurement function is designed to adaptively adjust the fusion mode of the low-frequency subband in the NSST domain. Finally, on the basis of the variance of the high-frequency subband in the NSST domain, the local region energy, and the visibility characteristics, the pulse-coupled neural network (PCNN) parameters are adaptively set to complete the fusion of the high-frequency subband in the NSST domain. Experimental results show that the QAB/F index of the algorithm is slightly lower than that of the comparison algorithm. However, the spatial frequency, SP, structural similarity, and visual information fidelity for fusion are improved by approximately 50.42%, 14.25%, 7.91%, and 61.67%, respectively, which indicates that the method effectively solves the low-frequency subband fusion mode. It also eliminates the need to manually set the PCNN parameters to solve this problem.

Nonuniformity is a key indicator of the image data quality of a remote infrared sensor. The uniformity correction residual of the image obtained by a pixel-level, dual-gain, time delayed and integration(TDI) detector is closely related to the linearity after reconstruction. In this study, the signal readout process of the infrared TDI detector was analyzed. A new image data reconstruction method is presented to obtain accurate normal parameters for each pixel. The detector output linearity was increased in all dynamic ranges, and the uniformity of the image was enhanced. Radiance calibration was performed, and the test data were processed. The result shows that the nonuniformity correction residual decreased from 4.1% to 1.2% based on radiance calibration.

A combination of infrared intensity and polarization images can more fully describe the characteristics of a detected scene and facilitate subsequent processing. An algorithm for fusing infrared intensity and polarization images using hybrid l0l1 layer decomposition is proposed. The algorithm consists of the following steps. First, multi-scale geometric transformations are applied to the infrared polarization and intensity images using hybrid l0l1 layer decomposition. Then, in the low-frequency characteristic subband image, the index local Gaussian distribution similarity is adopted as the low-frequency image fusion weight of the infrared polarization image, and the fused infrared polarization image is injected into the low-frequency infrared intensity image. Next, the local spatial frequency and local energy are used to fuse the high-frequency subband image, and the two fused images are combined by principal component analysis to obtain a high-frequency fused image. The final fused image is obtained by reconstruction. An experimental comparison reveals that the algorithm can be used to fuse images of different types with complementary features, and the quality of the fused image is clearly improved.

To solve problems caused by infrared image sensors, such a slow signal to noise ratio, background clutter interference and sensor noise, an infrared small target detection method based on max-median filters and Hough transform is proposed. This proposed method predicts a candidate target region using the method of Max-Median filtering with a single image frame. The candidate target region is processed by a mean filter and the residual region is processed by a Min filter. Then, the weighted Hough transform is used to detect targets with the accumulation of multiple image frames. Experimental results demonstrate that this proposed algorithm can detect small targets with a signal to noise ratio of approximately 1. The background movement does not have a significant impact on the effectiveness and robustness of the proposed method.

The development of a miniature near infrared spectrometer at a global level is herein summarized. The development prospect of the aforementioned spectrometer is then predicted. This spectrometer is expected to have high signal-to-noise ratio, high resolution, and high stability, as well as involve the use of new materials and technology, while ensuring its specialization in specific fields.

In the early stages of military infrared technology development, common modules were formed by standardizing the key components of thermal imaging systems in Europe and America. These common modules decreased the scope of choice between different technical proposals. However, it reduced the costs of researching, developing, operating, and maintaining infrared systems, and was beneficial to mass production. The SADA-series assembly was established in America based on the first-generation common module. The implementation of the horizontal technology integration (HTI) program based on the SADA assembly met the requirements of mass production and equipment for military thermal imaging. In this paper, the history and status of the common module in Europe and America is introduced. Common modules have become critical to the advancement of detector, microelectronics, and other related technologies.

The threats of infrared reconnaissance, surveillance, and guidance weapons to directly buried heat pipelines are increasing with the development of modern infrared technology. Therefore, it is necessary to conduct extensive research on thermal infrared camouflage technology for directly buried heat pipelines. Based on the principle of heat transfer, this paper establishes a numerical model of the temperature of the directly buried heat pipe and analyzes the influence of the buried depth of the pipe, the mixing of cold and hot fluids, and the coating of thermal insulation materials on the thermal infrared camouflage effect. The analysis results provide an important reference for the thermal infrared camouflage design of the directly buried heat pipe.