Quantum dots (QDs) are widely used in photoelectric detection, biomedicine, new energy, and other fields because of their quantum limitations, size, and surface effects. Recent years have seen midwave infrared (MWIR) quantum dots (QDs) become a focal point in infrared research. By adjusting and controlling their size, these QDs can extend their absorption wavelengths in the infrared spectrum. Therefore, the successful preparation of infrared QD materials and devices is crucial for infrared imaging, guidance, search, and tracking. This study first introduces the preparation and synthesis technology of five types of MWIR QDs materials, HgSe, HgTe, PbSe, Ag2Se, and HgCdTe, analyzes the size and morphology, lattice fringe, and infrared absorption spectrum characteristics of the QDs, and then summarizes the domestic and foreign MWIR QDs detectors. The device structures and preparation methods of the detector are summarized, and the photoelectric performance parameters, such as responsivity, detectivity, and response time, of the detectors are compared and analyzed. Finally, the development of MWIR QDs was discussed.

According to the theoretical model of continuous zoom optics, the continuous zoom calculation program is compiled, the initial solution of the zoom system is obtained, and the paraxial optical model is established. Through material selection and iterative optimization, a midwave-infrared continuous-zoom optical system consisting of only four infrared lenses and two planar mirrors was realized. The F number of the system is 4, the spectral range is 3.7.to 4.8 .m, the field of view (FOV) is 20°×16°.to.2.0°×1.6°, and the maximum aperture of lenses is 71.0 mm, the total weight of the lenses is 64 g, and the system envelope is 172 mm×108 mm. The system uses two binary surfaces for the achromatic. The athermalization design of the system was realized through the rational allocation of materials and active compensation. The medium wave infrared continuous zoom optical system has the advantages of light weight, short total length, small envelope, and good image quality in the temperature range of -40℃.to 60℃.

A complete star-viewing camera was designed using a NOIP1SN025KA CMOS detector to improve the accuracy of attitude satellites. The anti-irradiation experiment was conducted using 60Co-. radiation source under the environmental conditions of 24 ℃ irradiation temperature, 24 ℃ test temperature, and 37% RH test humidity. Subsequently, an optical system with a focal length of 500 mm, an F-number of 4, and a field of view of 2.4. are designed. The electronic system uses an FPGA as the core control device to control the CMOS output digital signal and transmits the signal back to the satellite data transmission system through TLK2711. The mechanical structure was mainly composed of a main mirror component, secondary mirror component, correction mirror component, baffle, and leg. The design scheme of the measuring cylinder (invar) supporting the secondary mirror was adopted to ensure that the interval change of the primary and secondary mirrors satisfied the tolerance requirements under the condition of temperature change. The mirror assembly was designed with radial and axial flexibility to ensure accuracy of the shape of the optical surface in the thermal environment. In the correction mirror assembly using pressure ring tangential pressing lens installation, the lens stress is small, good to neutral, impact, and vibration resistance, and can maintain good structural stability. The machine is connected to a satellite through the main mirror backplane. A star camera has two working modes: imaging and transmission of the threshold and coordinate information of the star point. Field imaging experiments showed that the camera exhibited good imaging quality, portability, and reliability. Approximately ten stars can be captured in the field of view, approximately 10 stars can be captured, and nine stars can be observed, which can effectively assist the star sensor.

This article is based on a multiband image fusion system and studies the alignment technology of the parallelism of the optical axis of the system. The five-axis parallel system includes a white light module, low light level module, short wave infrared module, long wave infrared module, and laser ranging module. The lowest light level module with the highest accuracy was 32.09. A parallelism deviation of less than 32.09 does not impact the system's usability. During installation and adjustment, the optical axis was aligned with the center of the collimator cross-target plate. This alignment produced an image size that is 99.89% of the maximum possible, which does not hinder the acquisition of image information. Finally, the system is verified experimentally using the developed platform. Experiments proved that the maximum deviation of the parallelism was nine, which is less than the maximum allowable error of the system. Therefore, this assembly and adjustment method had a certain reference value for the assembly and adjustment of similar products.

Reliability estimation of a thermal imager is becoming increasingly important in infrared weapon systems. The reliability estimation of a thermal imager based on prior information is studied using reliability expectation values and exploring experimental data as the prior information and a posteriori information, respectively, to evaluate the reliability of the thermal imager during development. The findings indicate that the method for estimating thermal imager reliability using prior information is both reasonable and feasible. This approach lessens the dependence on large field test sample sizes as typically required by classical statistics, thereby reducing resource costs through smaller field sample sizes.

Although infrared cameras can operate day and night under all-weather conditions compared with visible cameras, the infrared images obtained by them have low resolution and signal-to-clutter ratio, lack of texture information, so enough labeled images and optimization model design have great influence on improving infrared target detection performance based on deep learning. First, to solve the lack of an infrared target detection dataset used for surveillance applications, an infrared camera was used to capture images with multiple polarities, and an image annotation task that outputted the VOC format was performed using our developed annotation software. An infrared image dataset containing two types of targets, person and vehicle, was constructed and named infrared-PV. The characteristics of the targets in this dataset were statistically analyzed. Second, state-of-the-art target detection models based on deep learning were adopted to perform model training and testing. Target detection performances for this dataset were qualitatively and quantitatively analyzed for the YOLO and Faster R-CNN series detection models. The constructed infrared dataset contained 2138 images, and the targets in this dataset included three types of modes: white hot, black hot, and heat map. In the benchmark test using several models, Cascade R-CNN achieves the best performance, where mean average precision when intersection over union exceeding 0.5 1304 (mAP0.5) reaches 82.3%, and YOLOv5 model can achieve the tradeoff between real-time performance and detection performance, where inference time achieves 175.4 frames per second and mAP0.5 drops only 2.7%. The constructed infrared target detection dataset can provide data support for research on infrared image target detection model optimization and can also be used to analyze infrared target characteristics.

Human fall detection in infrared images is not affected by ambient light and has important research and application value in intelligent security. Existing fall detection methods do not fully consider the position change law of key points on the human body, which can easily cause false detections of similar fall movements. To solve this problem, we propose an infrared image fall detection algorithm based on an improved alpha pose. The algorithm uses the YOLO v5s object detection network to directly classify human poses while extracting the human body target frame and inputting the pose estimation network. It then evaluates it in combination with the position information and posture characteristics of the key points of the human skeleton. Experiments showed that the algorithm exhibited good performance in terms of accuracy and real-time performance.

Corona discharge images collected with night-type ultraviolet cameras are affected by the photographer’s environment and the degree of partial discharge, and the color of the discharge area is not only close to the background but also overlaps with the background, which makes it difficult to automatically segment corona discharge. This paper proposes a coarse-to-fine corona discharge ultraviolet (UV) image segmentation method. First, a deep-learning semantic segmentation model was constructed, and rough segmentation results of the corona discharge were obtained using a trained Unet network. Second, the UV image of the discharge region was converted into a gray image, and the rough segmentation result was accurately segmented based on the Otsu threshold segmentation method with foreground weighting. A total of 426 samples were tested, and all the corona discharge regions in the sample images were segmented using the proposed method. The error between the segmented discharge regions and the true value was close to 0. The proposed corona discharge segmentation method provides accurate data sources for the quantification and evaluation of corona discharges.

In order to effectively maintain and restore the edges and details of infrared images while removing the impulse noise, an iterative bilateral median filter based on intensity features and mode principle is proposed. In this method, based on the intensity features of impulse noise and the mode principle, the pixels that take the minimum and maximum values and are isolated on the intensity distribution of the neighborhood are recognized as noisy pixels. According to the weighted coefficients with respect to the spatial distance and intensity similarity, the noiseless pixels in the neighborhood and the pixels that have been denoised and restored are weighted by the frequencies, and the frequency weighted median is used as the estimated value of noisy pixels. Furthermore, the denoising processing is performed in the way of iterative traversal processing, which makes the most of the results of the previous traversal processing to remove high density noise. The experimental data confirm that the PSNR and EPI values and the visual effects achieved by the proposed method are better than the existing methods, with better denoising performance.

Porcelain insulators are widely used in power distribution lines, but they are susceptible to deg-radation during operation owing to long-term electromechanical stress and harsh outdoor environments. Infrared thermal imaging is an important live insulator degradation detection method. It has the advantages of convenient detection, safety, high efficiency, and non-contact operation. It has become an important method in power inspection. However, the thermal image characteristics of faulty insulators are not evi-dent and cannot be recognized directly by the naked eye. Therefore, in this study, we first conduct a temperature field simulation analysis of porcelain insulators in distribution lines and then propose an in-frared thermal image detection method for faulty insulators. A multi-scale template matching algorithm is used to locate and identify the insulators. The coordinate parameters of the insulator in the infrared image are obtained, the insulator is segmented and extracted by multi-scale template matching, and the tempera-ture of the insulator is extracted by least-square linear fitting. Combined with the relevant standards and simulation analysis results, the differences in the temperature states among multiple insulators were com-pared using a similar comparison judgment method to detect faulty insulators.

Evaluation of insulator operation is important for safely operating transmission lines. Ultraviolet (UV) imaging is a quantitative method for evaluating insulators. Therefore, a UV spot area assessment method based on an improved ANFIS for insulators was proposed. First, an insulator pollution discharge test platform was built to study the discharge intensity of the insulators at different test distances and gains. Second, an ANFIS(adaptive neuro-fuzzy inference system) model based on Bayesian inference is established using the gain and ultraviolet spot area as training data. Finally, a field verification test was conducted. The results show that this method has good prediction accuracy and test efficiency and is suitable for the quantitative evaluation of ultraviolet imaging for insulators, which provides technical support for the evaluation of the operating state of insulators.

area was calculated to determine the reliability level. The results show that only two of the five test stabilizers are in an abnormal state; specifically, component 3 of stabilizer 2 is abnormal, and component 1 of stabilizer 5 is abnormal. The relative temperature difference of component 3 of stabilizer 2 was 82.32%, and the corresponding reliability level was level 2, with low reliability; the relative temperature difference of component 1 of stabilizer 5 was 91.35%, the corresponding reliability level was level 3, and the reliability was extremely low. Comparative experimental results show that the recognition accuracy of the proposed method reaches 92.3% or higher, which is superior to that of the comparison method and has a greater application value.

To quantitatively detect defects using reflective continuous-heat-excited thermography, a heat conduction model of an object under continuous heat excitation was established, and the temperature increment-time relationship on the thermal excitation surface of the object was derived. Based on an analysis of the temperature increment-time relationship on the thermal excitation surface, the depth of the defects could be measured by nonlinear fitting of the temperature increment-time data. To test the feasibility of this method, a GFRP flat-bottomed hole specimen was fabricated and analyzed using reflective continuous-heat-excited thermography. The results show that this method is highly accurate in measuring the depth of defects.