A lead sulfide detector has the advantages of high short-wave infrared sensitivity and low auger noise. The lead sulfide film synthesized by chemical bath deposition can be compatible with the CMOS semiconductor process, which is beneficial for identifying low-cost and high-performance surface array detectors. However, the current research on lead sulfide detectors synthesized by chemical bath deposition primarily focuses on the larger unit detectors. In this study, the synthesis of lead sulfide film was based on chemical bath deposition. Lead sulfide photodetectors of 10–200.m size were prepared using an ion beam etching process and the photoelectric performance of the device was studied in terms of resistance, aspect ratio, line width, and other parameters. The results showed that the responsivity of the PbS photodetector increased gradually as the size decreased. Under the irradiation of 1550 nm short-wave infrared light, the responsivity of the 10 μm photodetector was 51.68 A/W, which was approximately 123 times the responsivity of the 200.m photodetector. Moreover, the PbS photodetector also had a good wide-range photoelectric response at visible light and 2.7.m infrared wavelengths. The micron-size detector of this study can provide support for the research of lead sulfide detectors.

With the development of infrared remote sensing technology, the demand for infrared detector arrays in various aerospace applications has exceeded the current developmental limit of single-module detectors. This problem needs to be solved by optical or mechanical splicing methods. Based on an advanced mechanical splicing technology, this study presents four design points of splicing structure and their influence on detector imaging for the development of an 8-module ultra-long linear splicing infrared detector. The specific design process of the design points and design results of the splicing structure are introduced in detail. The method for testing a splicing structure and a non-contact flatness test method are described; the test results are presented.

Cooled infrared detectors are widely used in the fields of intelligent optoelectronic equipment because of their fast response, high sensitivity, and wide range of detectors. However, the shock response will be caused by impact excitation in practical application scenarios. To ensure competency of a cooled infrared detector in a variety of complex and changeable harsh environments, it is necessary to study the adaptability of the shock response spectrum environment in the design stage. Based on the dynamics environment in the application, the loop force and displacement of the wire were analyzed through calculation and simulation, and the characteristics of materials were considered. The bonding wire materials and bonding wire loop were designed, wire bonding process was optimized, and finally, they passed the test of a shock response spectrum of 1000g.

To solve the problems of large volume and low detection efficiency of UAV hyperspectral imagers, a light and small multi-modal high-resolution hyperspectral imager is proposed. This study primarily introduces the optical system design, data acquisition, and real-time processing modules of the hyperspectral imager. The proposed imager satisfies the detection mode requirements in different fields, such as spectral characteristic analysis and target detection, by switching the scanning mode. A low distortion, high throughput, and compact spectroscopic optical system design is adopted to meet the weight and detection accuracy requirements of the UAV platform for the spectral imager. The product was processed according to the design requirements and the performance was tested simultaneously. Among these, the MTF reached 0.19 and spectral resolution was 3.5–5.4 nm. The ability of the system to detect real-time targets was verified through detection of impurities in various pipelines. The implementation results showed that the system achieved high-precision spectral anomaly target detection in 2048 pixel.2048 pixel scenes per second with a detection accuracy greater than 87%.

In a cooled infrared optical system, the cooled detector can be reflected through the front optical surface, such that the detector can detect its own reflecting image. The phenomenon of a dark spot with a bright edge and a dark center is called "Narcissus," which has a significant influence on image nonuniformity of the cooled infrared optical system. Narcissus should be strictly controlled in the process of optical design. In this study, a medium wavelength infrared optical system is taken as an example and a technical approach is proposed to effectively solve or reduce narcissus. A non-sequential mathematical model was established based on ZEMAX optical design software and simulation was conducted for analysis. The narcissus of the optical system was reduced from 40% to 13% after optimization and an engineering prototype imaging experiment was conducted. The analysis results of the simulation were consistent with the experimental phenomenon. This analysis method can accurately reflect the actual situation of Narcissus and can be used as a basis for judgment before the development and production of a cooled infrared optical system.

We propose an infrared image enhancement algorithm based on an improved local contrast (LC) significance detection algorithm and two-area histogram equalization to improve the visual effect of an infrared image, highlight detailed information, and suppress noise. First the LC saliency detection algorithm was combined with local entropy weighting to obtain the saliency map. Then, the saliency map was adaptively segmented into foreground and background regions by the K-means algorithm. Finally, the foreground sub-histogram was equalized using a local variance-weighted distribution. The background region was enhanced using contrast-limited adaptive histogram equalization. Experimental results showed that the subjective effect of the algorithm in this study was better than the current mainstream algorithms, and the objective evaluation parameters, such as peak signal-to-noise ratio, structural similarity, and entropy, were also improved.

Faults in power equipment are often observed during inspections as abnormal heat through infrared image detection. To address the problem of poor accuracy and efficiency in thermal fault diagnosis of power equipment using the Otsu method, an infrared image segmentation method based on variable spiral sparrow search algorithm (VSSSA) is proposed. VSSSA first uses tent chaotic sequences to improve the initialization. Then, Lévy flight and variable spiral strategy were introduced to enhance the optimization speed and exploration ability of the population. The effectiveness of the algorithm performance was verified using benchmark function tests. Finally, on the basis of VSSSA optimization of the two-dimensional Otsu function and double threshold segmentation of infrared images combined with adaptive region growth method, the accurate target region was further extracted. The experimental results of image segmentation demonstrated better accuracy of the proposed algorithm compared with that of other segmentation methods. This has certain practical applications.

In this study, an image demist algorithm based on limited light value and transmittance correction is proposed. The aim of the study was to address the issues of color distortion in the demist image obtained by dark channel prior demist algorithm when the filtering window is small, error in the selection of introduction factor and calculation of the transmittance of the bright area, and weak anti-noise performance of the demist image. First, the upper threshold of atmospheric light value A was set. Second, the best introduction factor was obtained by establishing the corresponding relationship between the introduction factor and structural similarity. On the basis of introducing the tolerance mechanism, the transmission optimization method was further proposed. Finally, based on the proposed defogging algorithm, a Gaussian filtering algorithm was incorporated, and the brightness of the defogging image was adjusted to improve the visualization effect. The simulation results showed that the PSNR and SSIM values and entropy value of the image obtained by the proposed algorithm were 9.9964 dB, 8.57%, and 0.3732 higher than those before the improvement, respectively; thus, the effectiveness and superiority of the proposed algorithm were verified.

The probability of detection, probability of false alarm, and signal-to-noise ratio (SNR) are three core indicators of the detection system for infrared point targets. Under the criterion of constant false alarm detection, this study analyzed the relationship between probability of detection, probability of false alarm, and signal-to-noise ratio(SNR) using single frame detection method, also called single threshold detection for infrared point target systems. This study focused on the analysis and calculation of the selection strategy of the second threshold under a multi-frame accumulation detection method (called the double threshold detection algorithm) and achieved a higher probability of detection and lower probability of false alarm with a lower SNR. A good solution was also found for the problem of detecting high-value targets with high threat and cost for false and missed alarms.

Infrared small target detection refers to the segmentation of small targets from infrared images. This is of significance in the application of fire detection systems, maritime surveillance, and other rescue systems. However, because of factors such as small target size, inconspicuous features, and complex background environment, the detection performance of current infrared small target detection algorithms is generally limited. To address this issue, an infrared small target detection algorithm based on the Laplacian pyramid multi-level transformer (LPformer) was designed in this study. During network iteration, small infrared targets are prone to losing texture detail information owing to their small size. The Laplacian pyramid was used to extract different levels of high-frequency boundary information from the original input infrared image. A structural information conversion module was then fused with the features of different levels in the backbone network to compensate for the lost texture information. Next, to further improve the discriminative ability of the network and suppress the false alarm rate while improving the detection accuracy, a channel-based transformer structure that takes each channel feature map as tokens was also adopted. This calculated the self-attention map along the channel dimension. Experimental results demonstrated that the detection performance of the proposed algorithm was higher than that of current advanced detection algorithms.

In this study, an infrared and visible image fusion using double attention generative adversarial networks(DAGAN) is proposed to address the issue of most infrared and visible light image fusion methods based on GaN using only the attention mechanism in the generator and lacking the attention perception ability in the identification stage. Using DAGAN, a multi-scale attention module that combines spatial and channel attentions in different scale spaces and applies it in the image generation and discrimination stages such that both the generator and discriminator can identify the most discriminative region in the image, was proposed. Simultaneously, an attention loss function that uses the attention map in the discrimination stage to calculate the attention loss and save more target and background information was proposed. The TNO test of a public dataset shows that, compared with the other seven fusion methods, DAGAN has the best visual effect and the highest fusion efficiency.

Human targets in thermal infrared images are easy to observe and have a wide range of applications. However, they are limited by the hardware of thermal infrared devices. The edges of human targets in the images are often blurred and the detection efficiency is poor. Simultaneously, because of the special imaging principle of thermal infrared, human target detection is vulnerable to the interference of heating and occlusion objects and the detection accuracy cannot be guaranteed. In response to the above issues, this study proposes a type of holistically nested edge detection (HED)-thermal infrared saliency human detection network. The network adopted the form of a similar HED network and detected human targets by adding the residuals of different proportions of the hole convolutional codec module. Experiments showed that the network can effectively detect human targets, accurately predict the edge structure, and also have high detection accuracy in an environments with heating objects and obstructions.

An image intensifier cannot pass an electromagnetic compatibility (EMC) test because of its weak anti-electromagnetic interference (EMI) ability. Therefore, it is speculated that abnormal phenomena, such as flickering, highlighting, and extinction of the fluorescent screen, similar to that observed in the test results may occur in the complex electromagnetic environment of a battlefield. This will likely interfere with night vision observation ability. To meet EMC requirements, this study first analyzed the EMC design weaknesses of an image intensifier based on EMC test results and determined the key factors affecting EMC. Next, high-frequency filtering, metal shell shielding, and low-impedance grounding were designed accordingly. Finally, the image intensifier was subjected to an anti-radiation interference test in a 200 V/m electric field; results indicated that the image intensifier was stable in the whole frequency range, verifying the acquired anti-EMI ability.

A composite insulator exhibits different heating characteristics under different defect types. In this study, a thermal defect detection method for composite insulators based on one-dimensional residual network and the central axis temperature data of composite insulators is proposed. First, the abnormal temperature rise range and position information of composite insulators under different defect types were statistically analyzed, and a sample set of composite insulator central axis temperature data under different defect types was obtained. Next, a one-dimensional residual network model was established. The dilated convolution was introduced into the residual block to expand the receptive field, and the efficient channel attention network (ECA_Net) was added to improve the feature weight with a high correlation with the defect category. Finally, numerical examples were verified and compared. Simultaneously, the t-distributed stochastic neighbor embedding (t-SNE) visualization method was used to reflect the effect of feature extraction on the model. The results showed that the model effectively captured the spatial dimension information of the central axis temperature data and adaptively extracted the features with high classification discrimination. Compared with ordinary convolution, auto encoder (AE), and support vector machine(SVM), the proposed model has improved recognition accuracy, good robustness and generalization ability. Thus, end-to-end composite insulator heating defect detection was realized.

Carbon dioxide (CO2) concentration monitoring is an important basis for carbon peaking and carbon neutralization. As one of the most widely used technologies in the field of greenhouse gas meas-urement, non-dispersive infrared (NDIR) detection technology focuses on effectively suppressing tem-perature drift and ensuring the stability and reliability of long-term monitoring data. The experimental results showed that the light power of the light source, strength of the gas absorption line, and central wavelength of the filter were easily affected by the ambient temperature. In this study, a temperature compensation method for infrared gas detection is proposed, and an analyzer for infrared detection of atmospheric carbon dioxide concentration is developed. Selecting 4.26.m as the CO2 gas absorption line with the central wavelength, the temperature compensation experiment of the analyzer was studied using the high and low temperature test chambers. A standard CO2 gas concentration was configured and concentration calibration experimental research was conducted on the analyzer. The measurement results showed that the infrared CO2 gas analyzer had the advantages of stable concentration measurement, sig-nificant temperature compensation, fast response, and a wide application range. The infrared CO2 gas analyzer provides reliable data support for terrestrial ecosystem carbon budget monitoring and other fields

In cases where sensors cannot satisfy relevant prescribed conditions, the point cloud data composing the inspection track of a substation robot cannot be accurately matched. Therefore, a three-dimensional point cloud registration method based on infrared image feature fusion is proposed for the inspection track of a substation robot. The gradient histogram of the robot motion direction and local self-similarity description are extracted, that is, the HOG and LSS features. Both types of features are fused using a multi-feature adaptive fusion method. The key points of the fused trajectory features and optimal target trajectory pose parameters are obtained through a preliminary registration of the three-dimensional point cloud. The optimized iterative nearest-point algorithm is used to accurately register the patrol trajectory and improve the registration results of the patrol trajectory pose. The experimental results show that the feature fusion effect of the proposed method is satisfactory and can improve the edge clarity of the image. The deviation index after fusion is less than 0.2, and the registration of key points for different image sizes is accurately completed. Moreover, the inspection track after the registration is consistent with the expected track.