Very-long-wavelength infrared (VLWIR) detectors have important applications in infrared imaging systems, spectral detection, and deep space exploration. Compared with medium-wavelength (MW) and long-wavelength (LW) IR detectors, VLWIR detectors exhibit significantly higher photocurrent and dark current, and their bias voltage becomes more discrete and sensitive. This study employs digital integration technology and multi-power-domain technologies to address the challenges of temperature sensitivity constrained by integration time and the wide bias voltage range required for VLWIR detectors. Based on a standard CMOS 0.13 mm process, a digital readout integrated circuit (DROIC) with a 320 × 256 array (30-mm pitch) and a 640 × 512 array (30-mm pitch) was designed and fabricated. In both DROICs, the pixel-level ADC is 16-bit, the charge capacity reaches 11.8Ge-, and the detector bias voltage range is extended to 2.2 V. Measurement results show that the power consumption is 53.1 and 149.6 mW (@100 Hz frame rate), respectively. Coupled with 320 × 256 and 640 × 512 VLWIR detectors, the average noise equivalent temperature difference (NETD) is 8.01 and 9.29 mK (half-well capacity), respectively. When the integration time is less than 10 ms, the dynamic range (DR) exceeds 90 dB. Compared with traditional readout integrated circuit, DROIC significantly expands the charge capacity, prolongs the integration time of VLWIR detectors, and effectively improves temperature sensitivity and dynamic range.

Due to high temperature coefficient of temperature (TCR), low noise level, and excellent CMOS process compatibility, vanadium oxide films are widely used as thermally sensitive layers in uncooled infrared bolometer detectors. To suppress the sunburn effect in uncooled infrared detectors under high-temperature fields of view, we optimized the detector fabrication process, enhanced substrate thermal treatment techniques, and studied their influence on the thermostability of vanadium oxide films. The results demonstrate that substrate thermal treatment can reduce the types and amounts of impurity bonds, improve the surface roughness of silicon nitride, and inhibit the crystallinity, stress, and resistivity deterioration of vanadium oxide films after thermal tolerance experiments. By comparing the time-dependent output differences between two detectors—with and without optimization—both subjected to prolonged high-temperature blackbody radiation, we conclude that substrate thermal treatment is beneficial for improving the thermostability of vanadium oxide and suppressing the sunburn effect in uncooled infrared detectors. This study provides a new direction for research on the reliability of uncooled infrared detectors.

This study systematically reviews research progress in the measurement of infrared radiation characteristics of aircraft targets, focusing on three key aspects: infrared radiation measurement calibration techniques, infrared radiation modeling of aircraft targets, and practical measurement cases. It outlines the technological evolution in both domestic and international contexts, identifies critical technical challenges, and forecasts future trends. Based on literature analysis and case studies, the evolution of calibration techniques from traditional blackbody-dependent methods to passive and broadband spectrum approaches is discussed, with high-precision intelligent online calibration proposed as a breakthrough direction. For aircraft infrared radiation sources, this study clarifies the radiation differences between jet-powered and rotorcraft drones, emphasizing that multiscale modeling and multi-physics coupled simulations are pivotal for improving simulation accuracy. By analyzing typical measurement cases, it highlights the role of multiband data fusion and non-cooperative target emissivity inversion in enhancing practical measurement precision. The findings reveal persistent challenges, including insufficient adaptability to complex environments and the absence of standardized modeling frameworks. Future research should prioritize the development of atmospheric transmission correction models for extreme weather conditions, intelligent calibration algorithms, and high-fidelity multispectral simulation systems. By integrating theoretical and applied achievements, this study provides technical references and research directions for infrared stealth design, anti-stealth detection, and aerospace safety.

With the continuous development of terahertz technology, the use of terahertz spectroscopy and imaging to detect explosives offers unique advantages in the field of security inspection. It is a noncontact, nondestructive testing method that can complement traditional detection techniques and holds great potential for further development. This article summarizes the fingerprint spectrum data of explosives studied by various institutions domestically and internationally, examines the factors affecting explosive spectrum measurement, and elaborates on the application of terahertz spectroscopy in detecting trace and hidden explosives. Additionally, it addresses the detection of different media, including the human body and explosives, using terahertz imaging technology, and provides a reference for research on terahertz nondestructive explosive detection.

Negative-electron-affinity GaAs photocathodes offer high quantum efficiency, low dark current, low emittance, and strong potential for long-wavelength expansion. They have a wide range of applications in vacuum optoelectronic devices and electron sources. For the transmission-mode GaAs photocathode, achieving higher quantum efficiency while maintaining a thinner emission layer is a challenge. In this study, a GaAlAs window layer with a novel subwavelength-periodic light-trapping structure is proposed to enhance the broad-spectrum light absorption of a transmission-mode GaAs photocathode with a thin emission layer. The optical properties of the photocathode were investigated primarily through optical simulations using the finite-difference time-domain method, and structural design research was conducted. Compared with the classical planar GaAs photocathode, the photocathode incorporating the subwavelength light-trapping structure showed a significant improvement in light absorption. By simulating and optimizing the line width, diameter, height, and arrangement of a nanoarray with a period of 600 nm, the optimal subwavelength array structure was determined to be a cylindrical array with a line width of 440 nm, a height of 490 nm, and a 1/4-period staggered arrangement. Its absorptance in the wavelength range of 500–930 nm reached 84.91%. Compared with the traditional planar structure, the improvement in optical absorptance in the near-infrared region was particularly significant.

To achieve omnidirectional observation, space telescopes typically require multiple attitude changes. However, such attitude changes lead to complex and variable external heat flows, which are highly detrimental to the heat dissipation of spacecraft instruments. Loop heat pipes are widely used in the aerospace field as components capable of long-distance, high-power heat transfer. For a space telescope equipped with several distributed instruments and operating with a total power consumption of up to 1300 W, multiple loop heat pipes, each 20 m in length and with a maximum heat transfer capacity of 400 W, were used for heat dissipation. Test results demonstrated that the loop heat pipes could transfer 400 W with a temperature difference of 13℃, effectively solving the challenge of unified heat management for distributed heat sources in spacecraft.

To meet the requirements of a photoelectric imaging system with low cost, wide operating temperature range, compact size, light weight, and low power consumption (SWaP), a catadioptric optical system with secondary imaging was designed using a half-structure design method. The front and rear groups were designed separately, with thermal dispersion and aberrations offset. The front group consists of a spherical primary mirror, a high-order aspheric secondary mirror, and a spherical lens in front of the primary image plane, while the rear group is a four-piece relay lens assembly. This system uses a 640×512 focal plane arrays with an F#3 medium-wave infrared refrigeration detector. The wavelength range is 3.7–4.8 m; the focal length is 200 mm; the obscuration ratio is no more than 1/3.33; the field of view (FOV) is 2.2°× 2.75°; the cold stop is 100% matched; the total length is less than 120 mm; the operating temperature range is –45℃ to 60℃. Across the full temperature range, the modulation transfer function (MTF) at the central FOV is ≥0.4@33 lp/mm, and at the edge FOV is ≥0.32@33 lp/mm. The equivalent temperature difference of Narcissus is ≤0.45℃ at an ambient temperature of 20℃.

Significant nonlinear radiation differences exist between infrared and visible images, resulting in low accuracy and poor robustness in traditional registration algorithms. To address this issue, a robust registration algorithm combining phase and edge features was proposed. To improve the accuracy of feature points, a phase-consistent superimposed moment map was established, and feature points were uniformly extracted from this map. Due to the limited descriptive power of descriptors derived from a single feature source, a descriptor combining both phase and edge features was constructed. Phase features were extracted based on a multiscale maximum index map, while edge features were extracted from the superimposed moment map. For registration, feature matching is performed to obtain the transformation relationship between the images. Experiments demonstrate that the proposed algorithm significantly improves the number of correct matching points, the correct matching rate, and overall accuracy compared to other methods.

Low-rank and sparse representations are widely used for hyperspectral anomaly detection. To fully exploit the spatial-spectral information of dictionary atoms, this study proposes a low-rank and sparse representation hyperspectral anomaly detection algorithm based on a spatial-spectral dictionary. To include all background categories in the spatial-spectral background dictionary, K-means clustering was applied. The feature similarity between pixels of each category and their neighboring pixels within a local window was calculated to obtain the residual difference constant matrix for each category, which was then used to compute the anomaly degree of each pixel. Representative atoms from each class were selected to form the spatial-spectral background dictionary, after which the abnormal and background components were separated using low-rank and sparse representations. The original data were reconstructed using this spatial-spectral background dictionary. Experimental results on five hyperspectral datasets demonstrate that the proposed method has good detection performance and can effectively improve the detection accuracy.

The RGBT object tracking utilizes the complementarity of visible light and thermal infrared modal information to improve tracking performance in scenarios such as cloud and fog occlusion, and illumination variation. However, due to significant differences in visible light and thermal infrared image features, most tracking algorithms cannot fully extract feature information, resulting in excessive redundant information after feature fusion. To address these problems, the SiamTAF adaptive fusion target tracking algorithm with RGBT and transformer was proposed. First, in the feature extraction stage, a transformer is used to enhance the last two layers of the AlexNet network with visible light and thermal infrared branches, enabling the feature extraction network to establish feature contextual dependency. Secondly, an adaptive fusion module is proposed that combines cross-attention and selection mechanisms to promote complementary fusion between the two modal features. Finally, to enable a linear cross-correlation operation to capture nonlinear similar features, nonlinear gated attention is added to the linear cross-correlation operation. Experiments on the GTOT and RGBT234 benchmark datasets show that, compared with algorithms such as MANet, DAFNet, and DAPNet, the SiamTAF algorithm is more robust when addressing thermal crossover and illumination variation.

To overcome the limitations of low infrared image resolution, poor texture information, and blurry details of small distant targets, we propose the YOLOv5s-CA algorithm, which modifies the YOLOv5s network structure from the perspective of the attention mechanism. The algorithm adds coordinate attention to the YOLOv5s model, enabling it to focus not only on the location information between channels but also on long-range spatial location information. By integrating this additional attention mechanism into the YOLOv5s network architecture and comparing it with the original YOLOv5s model, this study demonstrates the advantages of the algorithm in both speed and accuracy. Experimental results on a homemade infrared dataset for open-pit mining areas show that the model's mean average precision (mAP) reaches 0.948—1.4% higher than the original model—with an inference speed of 3.3 ms on a GeForce 2080 Ti device. Compared with other leading algorithms, this algorithm can maintain its speed while achieving high detection accuracy for infrared targets.

A photovoltaic module thermal spot defect detection model, RT-DETR-SRC, based on an improved RT-DETR framework, is proposed to address issues such as complex backgrounds, varying shapes and sizes of thermal spot defects, and low target feature saliency caused by reflective interference in infrared images captured by drones. Initially, based on the RT-DETR model, we introduced a fine-grained convolution, SPD-Conv, to improve the depth wise separable convolution module in the backbone network, refine defect feature extraction, and enhance the model's overall feature extraction capability. In the neck network, a RepBi-PAN-CARAFE structure is proposed to further improve detection accuracy A bidirectional cascaded feature fusion structure, RepBi-PAN, was adopted to enhance information exchange and feature fusion between deep and shallow features, while the feature upsampling operator CARAFE was introduced to capture and integrate contextual semantic information within a larger receptive field. Experimental results indicate that the mAP₅₀ and mAP₅₀:₉₅ of the RT-DETR-SRC model improved by 4.5% and 4.1%, respectively, over the baseline model, enabling more effective identification of hot spot defects in infrared images.

To address the problem of poor compatibility between spatial location and information extraction in existing substation equipment fault identification methods based on deep learning, this study proposes a fault identification method based on a UFPN-fuse network. First, the infrared image of the faulty device was segmented using the improved U-Net network, and the fault point features were extracted. Subsequently, the fault features and the original infrared image are fused in the improved FPN-fuse network to strengthen the contour of the fault point in the infrared image. In this way, fault location is achieved by enhancing the visual effect of the image while retaining the detailed information of the fault. Experimental results show that, compared with the comparison algorithms, the proposed algorithm achieves an average increase of 7.83% for SF, 7.48% for MI, 10.62% for AG, and 8.38% for VIF.

Defect size measurement has always been a popular topic in infrared thermography. Typically, images containing all defects are used for defect size measurements, which leads to significant measurement errors. We propose using clear images corresponding to the defects for measurement. First, the correlation function method was employed to filter out clear images corresponding to defects at different depths. Then, the half-width measurement algorithm was applied to achieve automatic defect size measurement. By measuring plastic specimens with defects of varying depths, it was shown that for a 20 mm defect, using images containing all defects resulted in a maximum measurement error of 12% and an average absolute error of 6.1%. Using clear images selected by correlation functions for defect size measurement reduced the maximum measurement error to 6% and the average absolute error to 2.6%. This method can effectively improve the accuracy of defect size measurement.

Excessive use of voltage transformers in 220 kV substations can lead to heating faults; therefore, a detection method based on infrared and ultraviolet radiation is proposed to ensure safe substation operation. Infrared and ultraviolet images of the voltage transformer were collected using infrared and ultraviolet imaging technologies, and a median filtering algorithm was applied to denoise the infrared images. After removing redundant information, the temperature and discharge characteristics related to infrared and ultraviolet radiation were calculated, and the resulting characteristic data were input into a radial basis function neural network. The parameters of the neural network were optimized using a quantum particle swarm optimization algorithm, improving the detection capability for heating faults in voltage transformers at 220 kV substations. Experimental results show that this method can effectively detect heating faults in voltage transformers. By analyzing the infrared and ultraviolet characteristics of the fault location, the heating fault can be identified and repaired in time, ensuring the safe operation of the substation.

Infrared detection technology is used for the qualitative and quantitative analysis of SF6 and its decomposition products—such as SO2, H2S, and HF—making it necessary to first analyze their infrared absorption characteristics and cross-absorption. In this study, the infrared absorption characteristics of SF6 were examined using the HITRAN2020 database, and the infrared cross-absorption among SF6 and its de-composition products (SO2, H2S, HF) was also analyzed. The results show that the infrared absorption band of SF6 is mainly located in the mid-infrared region at 590–640 cm-1, 940–952 cm-1, and in the far-infrared region at 330–370 cm-1. The peak absorption intensity in the mid-infrared region is more than 104 times greater than that in the far-infrared region. The infrared absorption spectra of these three bands are similar in structure, each composed of a P-branch, Q-branch, and R-branch. The absorption intensity of the Q-branch peak is much stronger than that of the P-branch and R-branch. The infrared cross-absorption bands of SF6, SO2, H2S, and HF are located at 590–600 cm-1, 330–370 cm-1, and 350–370 cm-1, respectively. In terms of absorption intensity, SF6 and SO2 are of the same order of magnitude, which is two orders of magnitude lower than that of H2S and four orders of magnitude lower than that of HF.

In the single-point diamond-turning process of monocrystalline silicon, tool wear affects the processing quality and efficiency of optical elements. The physical wear morphology of the diamond tools was observed using a scanning electron microscope and metallographic microscopy. The results showed that the diamond tools exhibited wear in stages: the stage of edge crushing, the stage of chip accretion, and the stage of tool surface grooving. Furthermore, based on the thermal–chemical wear mechanism, the formation mechanism of microgroove wear on the diamond tools were determined through energy spectrum analysis and X-ray diffraction. Finally, using a mathematical model that relates the surface roughness of single crystal silicon to the turning distance of the diamond tools, a tool life model was established.