The low accuracy of temperature measurements is a significant factor that restricts the application of infrared temperature measurements in precise temperature measurement fields. Presently, the primary objects for infrared temperature measurement are those with low reflectivity, referred to as low-reflectivity objects. To address the deficiencies of conventional infrared devices, a reflector infrared device was developed by replacing the lens of a conventional infrared device with a reflector, thereby enhancing its temperature measurement performance on low-reflectivity objects. Calculations and experimental results indicated that, after replacing the F/1 infrared lens with a reflector, the radiation collection capability and temperature measurement accuracy of the infrared device for low-reflectivity objects improved by approximately four times. Furthermore, when the field of view was filled with a low-reflectivity object, the temperature measurement results of the reflector-infrared device were independent of measurement distance. Infrared reflector devices are promising candidates for high-precision infrared temperature measurements in both scientific research and industrial applications.

To meet the temperature requirements of a high-sensitivity optical detection module during normal operation and overcome the difficulty of heat dissipation, this paper proposes a sensitivity analysis method that analyzes the parameters that affect the thermal characteristics of the detection module. First, the thermal balance equation of the detection module in the universe is established. According to the design parameters that affect the thermal characteristics of the module, seven temperature-related parameters of the detection module are extracted. The design parameters were then sampled using the Monte Carlo method, and 200 sets of samples were obtained using finite element analysis. Through a back propagation (BP) neural network analysis of the samples, a neural network model between the temperature distribution of the detection module and seven thermal design parameters is obtained. Finally, based on the established neural network model, a BP-Sobol' global sensitivity analysis is performed using the detection module. The analysis results show that the first-order Sobol' sensitivity values of the design parameters X1 and X2 are the largest and have the greatest impact on the detection module. The first-order sensitivity and total sensitivity of parameters X1, X5, and X6 vary significantly, and the interaction effect with other parameters has a substantial influence on the detector temperature output. This sensitivity analysis provides a strong guiding role for subsequent thermal design and experiments.

In order to achieve low illumination detection, a day and night compatible photoelectric scope is designed based on the electron bombarded active pixel sensor (EBAPS) of Northern Night Vision Co. Ltd. First of all, analyze the imaging characteristics of the EBAPS sensor, combine the characteristics of the night-mode split-focus surface of the sensor, and select the refractive initial structure to meet the imaging requirements of the device's split-focus surface by mechanical focusing. Use the CODE V optical design software to optimize the image quality of the EBAPS objective lens optical system, and achieve 0.3 mm focal separation in day and night mode by changing the glass air gap. The objective lens system's field of view angle is 10°×8.2°, the F number is 1.3, and the spectral range covers 450 to 10.00 nm, the clear imaging range is from 5 m to infinity. At the same time, an eyepiece with a ±5D visual degree adjustment range is designed, with an OLED micro-display. The whole photoelectric scope meets the recognition distance of not less than 300m for people walking upright in 10-3lx illumination space.

Refrigerated infrared detectors have a wide range of applications in military, industry, medicine, and other fields. Dewar components are composed of chipsets and dewars. A dewar can provide the necessary and reliable working environment and optical, electronic, and mechanical interfaces for the chip. When designing dewar components, the thermal power consumption of the chipset, static heat load of the dewar, thermal mass of the dewar, and heat transfer temperature difference must all be calculated to facilitate the selection of refrigerators and ensure the realization of indicators such as refrigeration time and power consumption under the working conditions of the full temperature zone. Currently, the calculation of indicators is manual; however, these processes are prone to errors and have low efficiency. Therefore, this paper proposes a method that utilizes software to calculate thermal power consumption according to relevant theories and uses the C# language to develop software. This software provides support for improving the accuracy and efficiency of thermal design. It also provides key indicators for the selection of refrigerators and the overall performance guarantee of the components. With further refinement, this method is expected to become more accurate.

Low-light image enhancement is an important problem in the field of image processing. The rapid development of deep learning technology provides a new solution for low-light image enhancement and has broad application prospects. First, the current research status and challenges in the field of low-light image enhancement are comprehensively analyzed, and traditional methods and their advantages and disadvantages are introduced. Second, deep learning-based low-light image enhancement algorithms are classified into five categories according to their different learning strategies, and the principles, network structures, and problem-solving capabilities of these algorithms are explained in detail. Third, representative deep learning-based image enhancement algorithms from the last six years are compared and analyzed in chronological order. Fourth, the current mainstream datasets and evaluation indexes are summarized, and the deep learning algorithms are tested and evaluated in terms of perceived similarity and algorithm performance. Finally, directions for improvement and future research in the field of low-light image enhancement are discussed and suggested.

Cryogenic refrigeration technology is important for space infrared astronomical telescopes. This article summarizes the development history of cryogenic refrigeration technology for space infrared astronomical telescopes, and the developmental status of the cryogenic system for the Origins Space Telescope (OST) is reviewed. The OST is a planned mid- and far-infrared space telescope to explore the origin of the universe. To achieve normal in-orbit operation, a combination of mechanical cryocoolers and adiabatic demagnetization must be used to maintain a low-temperature working environment. This review and analysis of the development status of adiabatic demagnetization refrigerators (CARDs) and various mechanical cryocoolers (e.g., turbine Brayton cryocooler, multi-stage pulse tube cryocooler, and J-T cryocooler precooled by multi-stage cryocooler) at home and abroad, introducing their different characteristics, will provide some references for the development of our country’s infrared space telescopes in the future.

To improve image quality in complex and dim environments, a network that leverages both the global information and textural details of polarized images through a strategy of multi-scale feature extraction and dual fusion, known as the Scale Feature Extraction and Dual Fusion Strategy Network (SFE-DFS-Nest), is proposed. The proposed network fuses polarized intensity images with polarization degree images. Initially, an encoder is constructed to extract multi-scale features from source images. Then, shallow features are fused using a lightweight Transformer, while deep features are integrated through a residual network. Finally, a decoder is built to reconstruct the fused features. Compared with existing image fusion networks, this network employs distinct fusion strategies for features at different scales. The experimental results show that images from dark and complex environments exhibited improved subjective visual comfort after fusion through this network. Furthermore, the fused images obtained using the proposed method outperformed those obtained using the compared methods in terms of objective evaluation metrics.

In the field of infrared technology, the fusion of infrared and visible images is important. To obtain infrared and visible fusion images with clear targets and rich details, this paper proposes an infrared and visible image fusion method based on an improved two-dimensional Kaniadakis entropy segmentation method and fast guided filtering. First, a simplified two-dimensional Kaniadakis entropy segmentation algorithm (S2DKan) is used to fully extract the target from the infrared image. Then, the non-subsampled shearlet transform (NSST) is performed on the infrared and visible images to obtain the low- and high-frequency sub-bands, and fast guided filtering is applied to the obtained high-frequency components to retain rich visible image details. The low-frequency fusion coefficient is obtained from the extracted target image and the infrared and visible low-frequency components using the low-frequency fusion rule. The high-frequency fusion coefficient is obtained from the enhanced high-frequency sub-band components using the dual-channel spiking cortical model (DCSCM). Finally, the fused image is obtained using the inverse NSST transform. Experimental results show that the fusion image obtained by the proposed algorithm has clear targets and background information and that the algorithm’s effect is stable.

Ground infrared target detection is crucial in the fields of high-altitude reconnaissance, intelligent perception, and ground strike, where the acquired ground infrared targets often appear in the form of irregular angles, resulting in low detection accuracy, ease of misdetection, and other problems. Therefore, this paper proposes an anchor-free-based ground rotating target detection method. Based on the anchor-free target detection model, a backbone network based on atrous convolution is constructed, which enhances the perception range and feature extraction ability of the model for ground rotating targets. After feature extraction based on void convolution, the attention dimension of the extracted feature is increased through external attention, and the extraction of higher-resolution features of the target is realized. The ground rotating target detection model achieved 90.6% detection accuracy on the HIT-UAV dataset, which optimized the detection performance of the anchor-free target detection model for ground rotating targets.

To address the challenge of fast and accurate detection of small infrared pedestrian targets at inclined viewing angles, a lightweight real-time detection network model for small infrared pedestrian targets (DRA-YOLO) was proposed. First, K-means++ anchor box clustering was utilized to adapt to targets of different size scales, thereby accelerating network convergence and improving detection accuracy. Second, different attention mechanisms were integrated into the redesigned feature extraction network to enhance feature location and computational efficiency. This was coupled with an improved feature pyramid structure to extract key features and enhance model stability. Finally, the neck was redesigned by eliminating down-sampling and reorganizing it with SimAM to form a new feature fusion structure. Moreover, the detection head was redesigned to suit the dataset used in this study. Comparative experiments showed that, relative to the original YOLOv5s model, the proposed method performed excellently on both self-made and public datasets. The mAP50 reached 94.5%, detection speed improved by 20.8%, model size was compressed to 10.1 MB (30.3% reduction), and GFLOPs decreased by 29.1%. These improvements facilitated the accurate and rapid detection of targets, effectively balancing model size, detection accuracy, and inference speed.

This article addresses the lack of heat transfer inversion analysis algorithms for current-bearing heat treatment furnaces. A one-dimensional unsteady heat conduction equation is used as the heat transfer mathematical model, and the quantum particle swarm stochastic optimization algorithm (QPSO) is employed to invert the heat transfer coefficient in the inverse heat conduction problem (IHCP) with the addition of random noise. In algorithm simulation experiments, the QPSO optimization algorithm exhibited high accuracy in solving the inverse problem of heat conduction. Meanwhile, with the addition of the wavelet filtering algorithm, the noise resistance of the inversion algorithm was significantly improved.

An improved super-pixel based FCM segmentation method for infrared image of power equipment is presented to solve the problems of low segmentation accuracy, slow convergence, poor selection of initial cluster centers and local optimization in traditional fuzzy C-mean (FCM) algorithm. First, a simple non-iterative clustering (SNIC) superpixel algorithm based on multi-feature fusion is used to pre-segment the image, and superpixels are used instead of pixels to express the image features, which reduces the subsequent processing complexity. Secondly, using the idea of maximizing the variance between classes, the gray value corresponding to the maximum value of gray histogram when the variance between classes is maximized is selected as the initial cluster center of the improved algorithm to avoid generating local optimal solution. Finally, combining the SNIC algorithm of multi-feature fusion with the FCM algorithm, the infrared image of power equipment is segmented. The experimental results show that the algorithm improves the under segmentation of the comparison algorithm on the contour of the device and the local high temperature area, improves the operation efficiency, and lays a foundation for the later fault diagnosis of power equipment.

To realize the fault recognition of an infrared image of a disconnector, this study uses an improved SLIC algorithm to segment and mark the fault area of the disconnector based on color space conversion. As a result, image segmentation accuracy was significantly improved. Based on HOG feature extraction, the support vector machine algorithm is used to classify an infrared image of a disconnector and determine whether it works in the normal state. For the disconnector in the normal state, the relative temperature difference method is used to determine its fault state. The greater the relative temperature difference, the more serious the fault. The experiments demonstrate that optimal HOG characteristic parameters yield the maximum accuracy of the imaging equipment. The fault diagnosis of an infrared image can be used to determine the fault and defect degree of the disconnector and provide maintenance. The model used in this study exhibits good accuracy and reliability.

Organic membranes are used as temporary substrates for the preparation of Al2O3 ion barrier films, and their thickness and compactness significantly affect the electron transmittance and electrical characteristics of the ion barrier microchannel plate (MCP). The micromorphologies of organic membranes with different thicknesses were analyzed using a step tester and metallographic microscope, and the electron transmittance and electrical characteristics of the ion barrier MCP were tested using comprehensive performance test equipment. The results show that the compactness of the organic membrane increases with increasing organic membrane thickness. When the thickness of the organic membrane is as low as 17 nm, the electrons can directly penetrate through the micropores in the organic membrane, which has low compactness; therefore, the MCP with the organic membrane and Al2O3 film shows good electron permeability. As the thickness of the organic membrane increases to 136 nm with high compactness, the MCP with the organic membrane and Al2O3 film can block almost all incident electrons. This explains why the threshold voltage and loss of current gain of the ion barrier MCP with Al2O3 film increase as the thickness of the organic membrane increases. The optimal thickness of the organic membrane is approximately 56–101 nm, as it satisfies the high-current gain and high-compactness ion barrier requirements of MCPs.

Due to the difficulty in replacing the grounding grid of urban integrated substations, any grounding grid failure can have a significant impact on the safety of the substation and surrounding buildings. This paper studies the terahertz tomography detection technology based on an improved generative adversarial network for fault detection of substation grounding grids. According to the characteristics of low resolution and high noise of terahertz image, uses terahertz tomographic image detection technology based on improved generative adversarial network. Firstly, the improved generation countermeasure network to improve the detail processing ability of the image; Secondly, uses the layer hopping connection method to effectively improve the context information reference ability of the image, so as to make the image more detailed and more specific. Finally, the method proposed in this paper is compared with other traditional processing methods, this method is more suitable for substation grounding grid fault detection.