Infrared imaging is an effective method for detecting gas leaks, enabling dynamic and visual observation of leakage occurrences. However, background interference and the intangible nature of gases often result in infrared images with indistinct gas-plume contours and reduced contrast. This study introduces a segmentation algorithm based on background modeling and density clustering that harnesses the spatiotemporal distribution characteristics of infrared gas images to segment gas regions in low-contrast infrared imagery. The foreground image was extracted by analyzing the matching relationship between the current frame and a sequence of frames using a Gaussian mixture model. Subsequently, a density clustering algorithm was applied to cluster the foreground image with spatial size constraints to filter out low-density regions. Morphological operations were performed to identify the gas-dispersion area. The experimental results indicate that the proposed algorithm can detect and segment low-contrast gas leaks within a scene. It significantly reduces noise and dynamic background interference, addresses voids in the gas region, and demonstrates distinct advantages over other algorithms. This offers a valuable reference for research on the segmentation of infrared images for gas-leak detection.

Infrared and visible light image fusion is an enhancement technique designed to create a fused image that retains the advantages of the source image. In this study, a depth image decomposition-based infrared and visible image fusion method is proposed. First, the source image is decomposed into the background feature map and detail feature map by the encoder; simultaneously, the saliency feature extraction module is introduced in the encoder to highlight the edge and texture features of the source image; subsequently, the fused image is obtained by the decoder. In the training process, a gradient coefficient penalty was applied to the visible image for regularized reconstruction to ensure texture consistency, and a loss function was designed for image decomposition and reconstruction to reduce the differences between the background feature maps and amplify the differences between the detail feature maps. The experimental results show that the method can generate fused images with rich details and bright targets. In addition, this method outperforms other comparative methods in terms of subjective and objective evaluations of the TNO and FLIR public datasets.

Object detection has long been a research hotspot in the field of computer vision, and the YOLO series of object detection models is widely used in numerous fields. However, most current image data for object detection are based on a single type of sensor, which makes it difficult to fully characterize the imaging scene. The detected objects contain limited useful information, especially under conditions of low illumination, night, rain, and fog. To improve nighttime object detection, our study proposed a multi-attention mechanism for infrared and visible images. This mechanism combines the CBAM attention mechanism with a Transformer to obtain rich local and contextual information and reduce false detections. To verify the effectiveness of the method, five current mainstream object detection algorithms were selected and tested on a public infrared object detection dataset. The mAP of the proposed method improved from 62.6% to 71.5% compared to the original YOLOv7. This study also produced an infrared–visible fusion dataset for nighttime object detection. On this dataset, the mAP improved significantly from 79.90% to 94.80% compared to the original YOLOv7.

Aiming at the detection difficulties of infrared images such as low signal-to-noise ratio, poor resolution, and much noise and clutter. We propose a lightweight infrared image target detection algorithm ITD-YOLO based on YOLOv7. Firstly, the ITD-YOLO algorithm redesigns the network structure, and re-adjusts the architecture of the feature extraction network and the feature fusion network. Crop out the large receptive fields corresponding to the deep layers in the original network, and adjust the model preset anchor frames based on the output of the reconstructed network feature map. The relationship between deep and shallow features in multi-scale feature fusion is changed to increase the weight of the detail information extracted by the shallow network in the fusion to improve the detection performance of smaller targets; then, PConv is introduced into the ELAN module to replace the conventional convolution to further reduce the model computation. Next, the model loss function is adjusted to PolyLoss to accelerate the model convergence and further enhance the detection performance for targets; finally, SIoU is used as the edge loss function to enhance the localisation accuracy for targets. The experimental results show that ITB-YOLO can effectively improve the detection effect, and the mean average accuracy is increased by 2.27% and 7.29% compared with YOLOv7s on FLIR and OSU datasets, respectively. The volume of the model obtained after the improvement is only 17.7 MB, and the computation volume decreases by 37.11%. Comparing with the mainstream algorithms, ITD-YOLO has been improved to a certain extent in all the indexes, and can meet the real-time infrared target detection task.

In this study, a method for overhead high-voltage wire segmentation and inspection is proposed to address the difficulty and low efficiency of overhead high-voltage cable inspection. This method utilizes the histogram bimodal method, region of interest extraction, and a wire optimization method that combines filters and image differentiation to extract and eliminate interference items, such as clouds, towers, and the ground in the image. The improved Hough line detection algorithm, which optimizes the voting and screening mechanisms, extracts the wires, and finally intercepts the required part of the wires through the topological relationship between the insulators and wires. The experimental results show that the proposed algorithm has ideal detection performance, with an average intersection-to-parallel ratio of 94.4% and a wire detection accuracy of 92.8%, meeting actual industrial production requirements.

The combat capability of a military at night is largely determined by the quality of low-light night-vision equipment. In contemporary military conflicts, various countries with sophisticated capabilities have been actively advancing low-light night vision equipment to achieve a substantial transition from “one-way transparency” to “exclusive control of the night.” This study systematically reviews the global development history and technological progress of low-light night vision equipment, focusing on the progression and current state of development in three countries: the United States, Russia, and France. It also explores specific investments, technological innovation paths, and actual applications in the development of low-light night vision equipment across these nations. By comparing and analyzing the developmental experiences of various countries regarding low-light night vision equipment technology, this study offers a reference and inspiration for the development of low-light night vision equipment in China. This comprehensive analysis demonstrates that low-light night vision technology is crucial in augmenting the combat capability of the military at night and guaranteeing national security, indicating that low-light night vision technology will play a crucial role in the military domain in the future.

Infrared detection is a passive detection technology with the potential to supplement radar in identifying targets. A crucial metric for this technology is its operating range, which has been a challenging yet popular area of research to evaluate. Considering the varied and starkly contrasting methods for assessing the performance of infrared detection systems, this review offers a thorough summary of existing evaluation techniques and categorizes them into five groups: visual inspection, laboratory calibration, field measurement, simulation evaluation, and cross-validation. In addition, a detailed analysis of the strengths and weaknesses of each evaluation method is provided. Finally, the challenges and future trends in this field are discussed and analyzed. This review is beneficial for beginners to start quickly and also provides a reference for further research in this field.

A self-anti-disturbance control method based on an elastoplastic friction model is proposed to address the problem of frictional nonlinear and external disturbances that affect the tracking performance of an optoelectronic stabilized platform. First, a spatial state model of a servo system based on elastoplastic friction is established. Second, the proposed elastoplastic model is used to compensate for the friction nonlinearity in the system via a feedforward method while initially suppressing the disturbance of the friction torque on the system and reducing the influence of measurement noise on the system, Third, a composite controller combining friction compensation and self-anti-disturbance control is designed based on this model. Finally, simulation experiments are performed on a servo system with friction. The simulation and experimental results show that the composite control scheme can improve the tracking performance of the photoelectrically stabilized platform. Moreover, the results verify the effectiveness and robustness of the proposed control method.

To accurately measure the concentration of trace gas methane (CH4) in ambient atmosphere, tunable diode laser absorption spectroscopy (TDLAS) technology was adopted, and a distributed feedback (DFB) laser with a central wavelength of 1653 nm was selected as the laser light source to build a CH4 detection system. For the detector noise and optical interference fringe noise in the system, radio frequency (RF) noise source, multiple averaging, and Kalman filtering were added to improve the detection accuracy of the system. The experimental results show that the calibrated CH4 concentration has an ideal linear relationship with the peak value of the second harmonic signal detected by the system by combining the long optical path multi-pass cell (MPC) and TDLAS technology. The minimum detection limit of the Kalman filtered system is 0.14 ppb when the integration time is 213 s. By determining the optimal parameters for adding RF noise sources and comparing multiple averaging techniques, a measurement accuracy of 144 ppb at an averaging time of 10 s was achieved. After applying Kalman filtering for data processing, the measurement accuracy reached 134 ppb, indicating that Kalman filtering can achieve high measurement accuracy.

Infrared detectors are being developed for high resolution and small pixels. To detect small pixels, an infrared optical system must have a small F-number. Therefore, based on the demand for small-pixel detection, this study designed an off-axis three-reflection infrared optical system with an F-number close to 1 using a Cook-type off-axis three-reflection structure. The primary, secondary, and tertiary mirrors of this optical system were all aspherical, with an F-number of 1.3, focal length of 60 mm, field of view of 1.6°×2.4°, and a wavelength band of 1.1–2.5 m. The modulation transfer function (MTF) of this infrared optical system design was greater than 0.6 at a spatial cutoff frequency of 100 line pairs per millimeter. The RMS value of the diffuse spot was smaller than the pixel size of the detector, with a distortion of less than 2%. The system design and simulation results indicated that the MTF of each field of view was close to the diffraction limit, meeting both imaging quality and small-pixel detection requirements.

Fastening of PCB circuit board screws is a key process in infrared thermal imager installation and adjustment. Its assembly quality directly affects the performance of the circuit board and image quality in an infrared thermal imager. First, a quantitative method for tightening the torque of the circuit board screws in an infrared thermal imager was proposed. The theoretical tightening torque of the circuit board screws was then calculated using the proposed method. Finally, combined with the tightening torque value obtained using the proposed quantitative method, ANSYS Workbench was used for the finite element simulation analysis. The results verify the safety of the circuit board under a load of the theoretical screw torque value. This study investigates a quantitative method of an infrared thermal imager circuit board, enabling the quantification of the screw tightening torque value during the assembly process of an infrared thermal imager PCB circuit board. This improved the assembly quality of each circuit component of the infrared thermal imager and the stability of the entire machine.

Uncooled infrared detectors have been developed rapidly in the military and civil fields. The vacuum packaging of detectors affects the life of components, with vacuum failure being the most common failure mode. In this study, based on the ceramic packaging of an uncooled infrared detector, the packaging structure and technology were analyzed, and the effects of the getter area and glue thermal weight loss on the degree of vacuum in the detector were studied using the Arrhenius equation as an accelerated life model. The experimental results show that increasing the getter area and volume, as well as using glue with low TG parameters to reduce the release of volatile gases, helps maintain the vacuum degree inside the detector and prolongs its vacuum life. This study provides a reference for the vacuum packaging of uncooled infrared detectors.

Infrared spectral gas analysis technology has gradually become the main analytical method for gas logging owing to its advantages of non-pollution, high detection efficiency, and accurate analysis. However, because of factors, such as numerous types of hydrocarbon gases in the formation fluid and a large concentration range span, the measured spectral data are complicated. Therefore, the pre-processing of the spectral data is crucial as it directly impacts the accuracy of the measurement results. Noise is a significant interference factor, and improving the noise reduction process for the spectral data is crucial. To solve this problem, this study proposes a wavelet transform optimized ensemble empirical mode decomposition (EEMD) combined with Savitzky-Golay filtering (S-G) for the infrared spectral noise reduction algorithm. This algorithm first uses EEMD to decompose the signal to obtain a set of IMF components. It then uses wavelet transform for wavelet threshold denoising on the IMF components. Finally, the denoised IMF components are reconstructed, followed by S-G. The experimental results show that the algorithm can not only remove the Gaussian white noise and impulse noise in the absorption spectrum but also improve the smoothness index of the absorption spectrum and enhance the accuracy of logging gas detection.

In this study, the properties of graphene photocathodes were investigated using a modified Hummers’ method enhanced by ultrasonic and hot water treatments to synthesize graphene powder. This powder, serving as a precursor, was used to form graphene films on the glass substrates of the photocathode window via spin coating. Subsequently, annular Ni/Cr electrodes were fabricated by electron-beam evaporation, resulting in the assembly of the graphene photocathode. Comprehensive analyses of photocathodes subjected to various preparation and processing techniques were conducted using atomic force microscopy, spectrophotometry, four-point probing, and photoluminescence testing. The results indicate that both the increased dispersion concentration and subsequent temperature reduction treatments significantly enhanced the absorption rate of the photocathode. Importantly, high-temperature reduction not only improves the surface smoothness of the photocathode assembly but also maintains the integrity of its crystal structure, reducing defects. This contributes positively to enhancing the emission characteristics of the graphene photocathode.

Real-time cross-sectional state verification of low-voltage equipment in an energy management system (EMS) is difficult in the maintenance of power equipment. Based on infrared images and deep learning, a real-time cross-sectional state verification method for low-voltage EMS equipment was proposed. The seed region growth method segments the red and green components of the real-time cross-section of the EMS low-voltage equipment in the infrared focal plane image to determine the abnormal area of the EMS low-voltage equipment. The invariant moment of the target shape in the abnormal area is extracted, and the difference in the active power of each branch before and after the disconnection of the EMS low-voltage equipment branch is used as the feature vector of the real-time section state check. A DC power flow model based on a deep neural network is constructed. The feature vector was input into the model, and the real-time section-state power flow calculation results were the output. The real-time section state power flow in abnormal areas was analyzed to determine if it was outside the limit, and a real-time section state check was completed. Experiments show that this method can determine the abnormal area of EMS low-voltage equipment. In addition, this method can accurately check the power flow of the real-time section state of the equipment to ensure the safety of equipment maintenance. Applying this method can improve the line qualification rate and voltage qualification rate.