To improve the resolution of the super Ⅱ image intensifier, factors such as the cathode input window, multialkali photocathode, microchannel plate (MCP), and phosphor screen were analyzed. The resolution was increased by reducing the cathode proximity distance, channel diameter of the MCP, and fiber size of the fiber optic faceplate and by depositing an anti-electronic dispersion film onto the output face of the MCP. These methods were verified experimentally. By continuously decreasing the cathode proximity distance continuously, the resolution can be gradually increased. On the condition that the cathode proximity distance is 0.08 mm, when the fiber size of the fiber optic faceplate and channel diameter of the MCP are reduced, with the deposition of a coated anti-electronic dispersion film on the output side of the MCP, a resolution of 72 lp/mm can be achieved, even reaching a maximum of 76 lp/mm. Thus, the resolution is improved by 33.33%.

The advantage of a fisheye lens is that it has a very large field of view and can provide more information. Nowadays, fisheye lenses are widely used in security, photography, aerospace, etc., especially in military and national defense. This paper firstly summarizes the imaging principle and development of fisheye lens, then analyzes the key issues, such as overflow of light or total reflection, and existing methods in the initial structure designing of modern fisheye lens, introduces the application and development trend of modern fisheye lens, and finally discusses recent crucial designing problems and the future of fisheye lens.

With the continuous development of optical plastic molding technology, optical plastic lenses are widely used in various optical systems. Optical plastics have the characteristics of convenient mass production, high design flexibility, light weight, and high impact resistance. This study further examines the characteristics of optical plastics and analyzes the feasibility of applying optical plastics combined with the characteristics of the optical system of laser-guided weapons. Thus, an optical system was designed based on aspheric optical plastics according to the specific optical system parameters, and the spot quality of the system was analyzed to meet the application requirements. Finally, to address the problem of optical plastics being sensitive to temperature changes, the laser-guided weapon detection system was modeled using the LightTools software. The sum–difference amplitude deviation and angle deviation of the system output under different temperature environments were analyzed to further verify the impact of optical plastics on the system and determine their applicability in optical systems.

The detection of anomalous heat source point targets in a wide range of infrared systems requires a balance between the pixel resolution and temperature sensitivity. Given that the scale of the detector is stable, the existing space-borne infrared payloads have insufficient sensitivity when the amplitude is large and small amplitude when the spatial resolution is high. In response to these problems, in this study, we propose a preliminary plan for detecting heat sources at abnormal points in a large field of view using a time-delay integration (TDI) algorithm to process images. Under certain conditions, the temperature sensitivity requirements for detection can be satisfied and a theoretical width of 217 km × 122 km can be achieved. A set of high-sensitivity infrared imaging experimental systems was built, and testing and simulation-based detection experiments were conducted. The results show that the sensitivity performance of this scheme is approximately 37 mK when a width of 202 km×.114 km is realized, which fulfills the requirements for detecting a large-scale abnormal point heat source. Considering the solar reflectance of the target and background, a pixel resolution of 200 m is used in practical applications, which corresponds to a width of 128 km×102 km.

Based on the imaging-effect simulation model of an optical camera, a pre-simulation method for typical fault effects of aerial infrared imaging camera is proposed. Based on an in-depth simulation of the imaging effects of the optical system, detector, and electronic circuit signals of an airborne infrared camera, imaging fault effects under different conditions were simulated, and an imaging performance evaluation index of typical faults was used for comprehensive verification and evaluation. The results show that the simulation model based on sensor imaging effect can effectively simulate multiple fault effects, which is consistent with the subjective evaluation results and plays an important role in guiding the use and maintenance of equipment during the finalization of the equipment test. Fault simulation analysis can effectively evaluate and predict typical fault effects in aviation sensors.

White light observation and sighting equipment exhibit a poor performance in regards to observing snow and backlighting conditions on plateaus. To address this, external filters, polarizing lenses, and shading tubes are used to improve the adaptability of the white light observation and sighting equipment by fully considering the characteristics of plateau tasks. Reasonable man–machine ergonomics designs, convenient operation, and portability allow meeting the needs of white light observation and sighting equipment under various environmental conditions in plateau areas.

Traditional infrared and visible fused images suffer from missing details and blurred targets owing to single features in complex environments. This study presents a method for fusing infrared and visible images based on a convolution neural network(CNN) combined with a non-subsampled contourlet transform (NSCT). Firstly, the infrared and visible target feature information is extracted by CNN, and the source image is decomposed by the NSCT at multiple scales to obtain its high-frequency coefficients and low-frequency coefficients. Secondly, the high-frequency sub-bands and low-frequency sub-bands of the source image are fused separately using adaptive fuzzy logic and local variance contrast in combination with the target feature image. Finally, the fused image is obtained by inverse NSCT transformation. We conducted a comparative analysis with five other traditional algorithms. The experimental results show that the proposed method performs better in several objective evaluation indicators.

To further improve the performance of target detection under air combat conditions, a detection algorithm, namely EN-YOLO v3, based on an air infrared target and the optimization of YOLO v3, is proposed in this paper. The algorithm uses the lightweight EfficientNet backbone network as the backbone feature extraction network of YOLO v3 to reduce the number of model parameters and training time. Additionally, CIoU is used as the loss function of the model to optimize the model loss calculation method and improve its detection accuracy. The results show that compared with the original YOLO v3, the optimized EN-YOLO v3 target detection algorithm reduces the model size by 50.03% and improves the accuracy by 1.17%. This can effectively improve the detection of aerial targets in infrared scenes.

To solve the problem of visual unnaturalness caused by contrast-limited adaptive histogram equalization (CLAHE) forced blocking, this study proposes an infrared image enhancement method based on semantic segmentation. The semantic segmentation network segments the entire infrared image into category blocks instead of traditional rectangular image blocks. Each category block is individually subjected to contrast-limited histogram equalization to reduce over-enhancement. Finally, a new edge transition method is introduced to avoid abruptness between category blocks. The experimental results show that the proposed image enhancement method outperforms other contrast algorithms in terms of contrast and entropy and avoids the visual unnaturalness of traditional CLAHE with better visual effects.

A visual attention mechanism (VAM) can quickly highlight region-of-interest targets; therefore, it is reasonable to introduce visual attention into hyperspectral image (HSI) anomaly detection tasks. By adjusting a bottom-up VAM model in three aspects, namely sampling method, band selection, and local spectral features, a more applicable VAM model for calculating the saliency of hyperspectral images was constructed. The resulting VAM is called bottom-up hyperspectral saliency map (BUHS). To solve the problem of background parameter estimation in the RX(Reed-Xiaoli) algorithm, which is susceptible to interference, BUHS was used as a Gaussian weighting parameter for the original image, in which new parameters of the RX anomaly method were calculated. Compared to the traditional RX, the background parameters are more accurate. The experimental results on five HSI datasets show that the proposed method can effectively identify potential anomaly targets and improve the RX algorithm with a higher detection accuracy and lower false alarm rate.

Current methods of on-site detection of airtightness of building windows cannot ensure that the airtightness grades of all windows satisfy the standard. Moreover, there is a lack of efficient and convenient detection methods. Thus, we proposed an on-site method to detect the airtightness performance level of windows. In this study, an infrared image of the windows is collected using a thermal imager, the abnormal area in the image is detected and the defect area is calculated, then an infrared detection model for window defects is established. Based on the experimentally measured indoor–outdoor temperature difference, the defect area of the window and air infiltration, a calculation model for the air infiltration of windows is built. The model is combined with the infrared detection model of exterior windows defects to obtain the air infiltration of the window, and the on-site detection of the windows airtightness performance is realized and then preliminary determine of whether the window meets the corresponding airtightness performance level, which improves the efficiency of the on-site inspection of the airtightness performance of windows and provides a new method for the on-site determination of the airtightness performance level of windows.

Methane is an important energy source for modern industrial production and social life, and its effective detection and segmentation are important for the timely detection of methane leaks and identification of its diffusion range. To address image problems such as blurred contours of methane gas, low contrast between leaking methane gas and the background, and susceptibility of the shape to atmospheric flow factors under infrared imaging conditions, this study proposes an infrared image segmentation network (attention branch feature network (ABFNet)) incorporating attention branch features to achieve methane gas leak detection. First, to enhance the feature extraction capability of the model for IR methane gas images, a branch feature fusion module was designed to fuse the output features of residual modules 1 and 2 with residual module 3 in a pixel-by-pixel summing method to obtain rich and detailed feature expressions of IR methane gas images to improve the model’s recognition accuracy. Second, to further accelerate the inference speed of the model, the 3×3 convolution in the standard bottleneck unit was replaced with a depth-separable convolution to significantly reduce the number of parameters required for real-time methane gas leak detection. Finally, scSE attention mechanisms were embedded in the branching feature fusion module to focus more on the edge and center semantic information of the diffusion region to overcome the problem of low contrast of the blurred IR methane gas contours and improve the generalization ability of the model. The experimental results showed that the quantitative segmentation accuracy of the proposed ABFNet models AP50@95, AP50, and AP60 reached 38.23%, 89.63%, and 75.33%, respectively, with improvements of 4.66%, 3.76%, and 7.04%, respectively, compared with the segmentation accuracy of the original YOLACT model. The inference speed reached 34.99 frames/s and met the demand of real-time detection. The experimental results verified the effectiveness and engineering practicality of the proposed algorithm for infrared methane leak detection.

In the process of security inspection, rapid and accurate identification of prohibited items is conducive to maintaining public security. To address the problems of stack deformation, complex background interference, and small-sized contraband detection in X-ray luggage images, an improved model for contraband detection is proposed. This improvement is based on the YOLOX model. First, an attention mechanism was introduced into the backbone network to enhance the ability of the neural network to perceive contrabands. Second, in the neck part, the multi-scale feature fusion method was improved upon, and a bottom-up structure was added after the feature pyramid structure to enhance the performance ability of the network for details, thereby improving the recognition rate of small targets. Finally, the calculation method based on IOU loss was upgraded in view of the disadvantages of the loss function calculation. The weights of various loss functions were also increased according to the characteristics of the contraband detection task, and the punishment of network misjudgment was increased to optimize the model. Upon using the improved model on the SiXray dataset, an mAP of 89.72% was attained and a fast and effective FPS arrival rate of 111.7 frame/s was achieved. Compared with mainstream models, the accuracy and detection speed of the proposed model were improved.