Improving the anti-decoy jamming ability of infrared-guided missiles is an important research topic for ensuring combat effectiveness. This paper first introduces the principle and process of the infrared seeker response to a decoy. Subsequently, it describes the course of their confrontation and anti-confrontation between infrared-guided missiles and decoys and extracts the anti-decoy jamming technical approach of infrared-guided missiles. Finally, a typical anti-decoy jamming strategy for an infrared-guided missile was analyzed and summarized.

The increasing use of infrared detector components in military and space applications has generated growing demand for reliability assessments. However, the lack of standardized evaluation methods for component reliability and the challenges in assessing key reliability indicators pose complexities for research institutes. To address this issue, we conducted a comprehensive review of recent reliability assessment research conducted by leading foreign research institutions on infrared detectors, consolidated the results of accelerated life tests on primary subassemblies, and proposed several approaches to enhance the evaluability and precision of components, including conducting accelerated tests on key subassemblies: FPA, dewar and cooler, employing essential research methodologies such as standard usage profiles and acceleration factors, and integrating engineering expertise, theoretical techniques, statistical tools, and field data feedback to advance component reliability.

In this study, the main factors affecting the signal-to-noise ratio (SNR) of a super-second-generation image intensifier are investigated through theoretical analysis combined with experimental validation. The results show that photocathode sensitivity, brightness gain, the opening area ratio of the Microchannel Plate (MCP), the secondary electron emission coefficient at the first electron collision, and the MCP tilt angle all influence the SNR. The SNR increases with higher photocathode sensitivity—rising by 49.6% when the sensitivity increases from 356 A/lm to 1013 A/lm. Conversely, the SNR decreases with the increase of luminance gain—dropping by 12.3% as luminance gain increases from 5000 cd·m-2·lx-1 to 20000 cd·m-2·lx-1. The SNR is negatively correlated with the MCP noise factor. A lower noise factor is associated with a larger MCP opening area ratio and a higher secondary electron emission coefficient during the first electron collision. Additionally, the MCP noise factor first decreases and then increases with increasing MCP tilt angle. Based on these findings, the optimal values for the MCP opening area ratio, the secondary electron emission coefficient at first collision, and the tilt angle were determined within a defined range of sensitivity and brightness gain. Under these optimized conditions, the MCP noise factor is reduced by 38.0%, and the SNR is improved by 26.9% compared to the standard configuration of the super-second-generation image intensifier. This work lays a strong foundation for further enhancing the SNR performance of super-second-generation image intensifiers.

In recent years, with the rapid development of indium gallium arsenide (InGaAs) detectors, shortwave infrared (SWIR) technology has demonstrated unique advantages in fog penetration and night-time detection, leading to widespread applications in night navigation, assisted driving, military reconnaissance, and other fields. The introduction of 1280×1024 pixel InGaAs detectors in China has underscored the growing importance of developing SWIR optical systems with large-area arrays and large relative apertures. To address the demands of night vision imaging in complex environments, this study presents the design of a SWIR optical imaging system based on a 1280×1024 pixel InGaAs detector with a 15 m pixel pitch. The system enables high-resolution, wide-field imaging and is built upon a classical double Gaussian optical configuration. By employing a carefully selected combination of commonly used visible-light optical materials, the design achieves apochromatic correction across the SWIR band. The resulting optical system offers key advantages, including compact size, large relative aperture, high resolution, and excellent manufacturability.

Micron-scale chips are widely used in large-scale industrial production because of their low cost and mature technology. However, in the manufacturing field, design studies on digital lithography projection objective lenses with micron-level resolution are relatively few. In this paper, a digital lithography micro-projection objective lens with micron resolution was designed using ZEMAX. For the commonly used 405 nm lithography light source, the system achieves a resolution of 0.625 m, enabling more precise structural processing. The imaging distortion is reduced to 0.0159%, significantly improving overall image quality. The designed objective lens has a magnification of –0.0714 and a numerical aperture of 0.02, meeting the requirements for most micron-scale chip manufacturing processes. Additionally, a microlens array was designed to ensure uniform illumination, minimizing the effects of uneven lighting. Tolerance analysis shows that 90% of the manufactured lenses achieve an MTF greater than 0.7692, satisfying machining accuracy requirements.

To meet the requirements for a low-cost, compact, dual-field, and wide-temperature-range medium-wave infrared (MWIR) seeker, a dual-field MWIR optical system was designed based on a 640×512 pixel detector with a pixel size of 15 m. The system adopts a four-lens imaging configuration using silicon/germanium (Si/Ge) materials. To achieve dual-field switching and athermalization over a wide temperature range, a single-lens axially moving zoom structure was employed, incorporating a refractive/diffractive hybrid optical element and an aspherical surface. Prototype results show that the system operates in the 3.7−4.8 m spectral band, with an F-number of 3.97 and focal lengths of 65 mm and 21.5 mm, offering a 3× optical zoom. The corresponding field of view ranges from 8.4°×6.7° to 25.1°×20.2° at 33 lp/mm. The on-axis modulation transfer function (MTF) is no less than 0.3, while the off-axis MTF at 0.7 field is no less than 0.22. The cold stop efficiency reaches 100%. The system maintains good imaging performance across a wide temperature range (–40°C to +60°C) without significant Narcissus effects. The overall dimensions are no more than ϕ50 mm × 72 mm, and the total weight does not exceed 36 g. The system features a compact structure, ease of fabrication and alignment, high production yield, and strong feasibility for engineering implementation. Its performance metrics meet the requirements of practical applications.

To address the problem of gradient vanishing in recurrent neural networks and the limited receptive field of traditional convolutional neural networks, this paper proposes a spectral–spatial feature extraction method that incorporates multi-scale convolutional filters. The method consists of two main components: spectral feature extraction and spatial feature extraction. In the spectral feature extraction stage, a bidirectional long short-term memory (Bi-LSTM) network is combined with a band-grouping strategy. This approach mitigates the gradient vanishing issue caused by excessive network depth. In the spatial feature extraction stage, multi-scale convolutional filters are introduced based on a convolutional neural network (CNN), allowing the model to capture both fine details and global structural information. Additionally, shallow features are fused with deep features to further enhance classification performance. Experimental results on two datasets demonstrate that the proposed method effectively improves classification accuracy.

The purpose of an infrared-visible person re-identification task is to match RGB and infrared images of the same identity. Because of the different imaging principles of the two modalities, it is difficult to efficiently extract discriminative modality-shared features. To address this issue, this study proposes a Modality-shared feature enhancement module and a global feature enhancement module that jointly extract enhanced discriminative global features. First, a modality-shared feature enhancement module is added to the backbone network to alleviate modality information and enhance modality-shared features with contextual information. Second, the global feature enhanced module encodes global features and jointly optimizes the loss function to further enhance the discriminative power of the global features while mining pattern features. Finally, the mutual mean learning method was used to reduce modality differences and constrain the feature representation. Experiments on mainstream datasets show that the proposed method achieves higher accuracy than existing methods.

In infrared imaging, small targets often exhibit indistinct contours and sparse texture information, presenting a significant challenge for identification based solely on their inherent characteristics. To address this limitation, a novel mixed-frequency feature detection (MFFD) model is proposed. This model substantially improves small-object detection performance by leveraging both the contextual information of the target and its surrounding background. The MFFD model introduces a mixed-frequency extraction module that enhances small-target recognition by integrating global low-frequency semantic features with local high-frequency target details. Additionally, a multi-stage fusion module is employed to effectively coordinate feature interaction and integration across multiple levels, thereby improving semantic understanding and spatial information fusion. On the publicly available NUAA-SIRST and IRSTD-1k datasets, MFFD-Net outperformed five other deep learning-based methods. Compared to AGPC-Net, MFFD-Net achieved significant improvements in IoU and nIoU metrics. For the NUAA-SIRST dataset, increases of 4.42% and 4.33% were observed, respectively, while for the IRSTD-1k dataset, the corresponding improvements were 3.63% and 6.38%. These results demonstrate the strong potential of the proposed model for detecting small objects in complex infrared backgrounds.

Infrared (IR) thermal imaging target detection is essential for enabling robots to conduct all-weather inspections in field environments. This paper addresses two key challenges: the limited computing power of embedded systems onboard robots for real-time detection, and the low resolution of small targets in thermal imaging. To address these challenges, a lightweight detection algorithm based on an improved YOLOv7 framework is proposed. First, the network structure is pruned to enhance real-time performance on embedded devices. Subsequently, the backbone is optimized by integrating adaptive convolutional layers and a batchless normalization module. To improve small-target detection accuracy, multi-rate dilated 3D convolution is used to extract high-resolution scale-sequence features, which are subsequently fused via a Feature Pyramid Network (FPN). Finally, the SIoU-based position regression method is introduced in the prediction stage to improve regression speed and accuracy. Experimental validation on the NVIDIA Jetson Xavier NX platform using a nighttime thermal imaging dataset shows a 162% improvement in FPS, with only a 1.95% reduction in mAP compared to the original YOLOv7, meeting the requirements for real-time detection.

To address the challenges of difficult feature extraction and low contrast in infrared small-target images, an improved attention mechanism is proposed. First, a parallel dual-channel reverse attention mechanism is designed. Based on the reverse attention concept, one branch processes features in the order of spatial attention followed by channel attention, while the other branch follows the reverse order—channel attention followed by spatial attention. These two independent branches are used together to merge their outputs. Second, a parallel dual-channel reverse attention mechanism is introduced into the Res2Net structure, and an improved region strength level module is added to Res2Net. Third, the loss function considers the global and local constraint loss functions. The simulation results show that improving the attention mechanism has good visual effects, and the accuracy and ROC detection performance are better than those of other algorithms.

Infrared small-target detection, a complex and critical task in computer vision, faces numerous challenges—including tiny target sizes, low contrast, severe background noise, and limited data availability. These factors significantly impair detection accuracy and real-time performance. Existing deep learning–based algorithms, which predominantly adopt segmentation paradigms via deep encoder–decoder architectures for generating segmentation masks, often exhibit limited precision in complex scenarios due to inadequate feature representation and learning capabilities. Inspired by the notable success of diffusion models in artificial intelligence, this paper introduces a novel approach by reframing infrared small-target detection as a generative task and proposes a conditional denoising network, termed diff-ISTD. By leveraging the strengths of progressive denoising and image reconstruction, diff-ISTD captures the deep statistical properties of infrared images, enabling more precise identification of weak and ambiguous small-target features. The proposed network consists of conditional branching modules for extracting prior knowledge from infrared inputs and denoising branches for refining noisy segmentation masks. In addition, a parallel dual-dimensional self-attention (PDSA) block is introduced to integrate spatial and channel information, significantly enhancing the model's sensitivity to global structures and local details. This design effectively addresses the challenges of target blurring caused by resolution limitations and environmental variability. Comprehensive experiments demonstrate that, under rigorous detection conditions, diff-ISTD outperforms current state-of-the-art segmentation methods in terms of performance and detection efficiency, offering a promising direction for advancing infrared small-target detection technologies.

Owing to the limitations of the manufacturing process and materials, the response of each detection unit of infrared focal plane arrays (IRFPA) is inconsistent when receiving the same intensity of infrared light, causing the generated images to contain significant non-uniform noise. This study proposes an algorithm for correcting infrared non-uniformity based on motion-blurred images. The sliding window is used to separate the non-uniform noise and the image scene information on the infrared motion-blurred image, and image correction is realized using the noise information. The experimental results show that the nonuniformity of the corrected image is reduced by 4.2% compared to that of the original image, which greatly improves the image nonuniformity.

This paper proposes a semantic segmentation model called VA-Unet, designed to address the challenges of complex backgrounds, slight spot separation, complex feature selection, and low segmentation accuracy encountered in ultraviolet (UV) detection tasks of electrical equipment. VA-Unet incorporates the VGG16 feature extraction module and transfer learning to accelerate training and enhance the model's generalization capability. Additionally, an Attention Gate is integrated to improve segmentation precision by focusing on relevant features, enabling accurate detection of UV discharge spots in images. To address the issue of sample imbalance in the UV discharge spot dataset, VA-Unet employs a hybrid loss function in place of a conventional single loss function. Experimental results demonstrate that VA-Unet achieves superior performance in the precise localization and accurate segmentation of UV discharge spots. The model attains an IoU of 84.09%, PA of 88.20%, and F1-score of 91.35%, representing improvements of 14.41%, 3.24%, and 9.22%, respectively, compared to the baseline U-Net model.

Glass packaging is a critical component in the production of silicon-based organic electroluminescent organic light-emitting diode (OLED) microdisplays. This study investigates the impact of two curing methods—thermal curing and UV curing—on the reliability of glass encapsulation technology. Key parameters, such as encapsulation efficiency, alignment precision, bonding strength, resistance to environmental aging, and photoelectric performance, were evaluated for both encapsulation systems. Consequently, the UV-curing glass encapsulation outperforms thermal curing in terms of resistance to environmental aging, bonding strength, alignment precision, and manufacturing efficiency. However, the difference in photoelectric performance between the two methods is negligible.

Autoclaves are commonly used pieces of equipment in fields, such as wet metallurgy and chemical engineering, and face the threat of leakage. Once a leak occurs, it can lead to unstable pressure inside the autoclave, and in severe cases, it can even cause explosions, posing a threat to production safety. In this regard, we propose a wet metallurgy autoclave leakage detection algorithm that combines infrared image filtering, segmentation, and frame difference methods to extract morphological changes in the leaking gas-phase medium. The real-time leak location was determined using a multivariate Gaussian analysis. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 95% within a 1-second time span after a leak occurs and a detection accuracy of 98% within a 2-second time span, effectively pinpointing the precise location of the leak source.