In recent years, image sensors are more and more widely used in ultraviolet imaging, especially the ultraviolet image sensors based on CCD and CMOS have attracted intensive attention of researchers. The progress of semiconductor technology and the development of nanomaterials further promote the research of ultraviolet image sensor. In this review, the research progress of ultraviolet enhanced image sensor at home and abroad is reviewed, and several materials enhancing the ultraviolet response of the device are introduced. In addition, the applications of ultraviolet image sensor in biochemical analysis, atmospheric monitoring and astronomical detection are briefly summarized, and the challenges faced by CCD/CMOS image sensors in ultraviolet detection are discussed.

In recent years, the type II superlattices (T2SL) infrared detector has experienced incredible improvements in material growth, device structure design, device fabrication techniques, which make the T2SL become the most popular infrared detector material, besides HgCdTe. This article briefly introduces the advantage of the T2SL material, summarizes the international research status in T2SL-based photodetectors, reviews the history of technology development of T2SL-based photodetectors and analyzes the drawback of the research of T2SL material and device technology in China.

In infrared signal processing, the original output image of the infrared detector has serious non-uniformity, high noise, and low contrast, which cannot meet the requirements of the image quality of ammunition. To solve problems such as large amounts of infrared image signal data, complex algorithm, and strong real-time performance of the system, the integrated infrared signal processing circuit is developed for reducing the volume and improving performance.

A large field of view, multi-channel, lightness, and miniaturization have all become critical requirements for satellite-borne surveying and mapping optical systems. According to the above-mentioned requirements, a design method that uses the field of view to split light and then uses a narrowband dichroic plate to split light is proposed, and an optimization function for automatic de-occlusion, lightness, and miniaturization is constructed. This method was used to design an off-axis three-mirror four-channel optical system with spherical primary, secondary, and tertiary mirrors. For the ground image, the focal length was360mm, relative aperture was 1/6, field of view was 13°×5°, and working waveband was 0.4– 1.1.m. The element resolution was 5 m, and the distortion of the full field of view was less than 5%. The entire system installation and adjustment were completed according to the tolerance analysis results. After processing and installation, the measured modulation transfer function (MTF) curve of the100 lp/mm entire system was greater than 0.25. The system had good image quality and it occupied only 245 mm×423 mm and the entire weight was only 13.82 kg. This shows that the system had excellent characteristics of a large field of view, no obstruction, lightness, and miniaturization.

The search efficiency of a sea target helicopter can be studied based on the searching method and search width of the optical recon pod of the helicopter. According to the characteristic performance of the optical recon pod and helicopter，two basic methods of searching and covering square are proposed for the helicopter to search the sea target. According to the developed search covering square，the commander can determine how to configure the recourses to attain the optimal efficiency. In addition, the processing method for image stitching for the scanned search area is introduced to identify the target of interest in the easy search area.

It is necessary to test and analyze the distortion of an infrared imaging system in a laboratory because the ability of the system to play a role on the platform is dependent on the distortion control of an infrared imaging system. We have proposed a method based on a test of sub-pixel precision and precise angle measurement to calculate distortion using centroid difference data. It is laborious to test the distortion of an infrared system with a large field of view using a method which requires the computation of a large number of coordinates of points. Furthermore, the location of the field view could be a problem for the partial distortion method. By using the proposed method, the aforementioned problems can be addressed. We applied the method to evaluate a thermal system and the results were in agreement with those of the simulation software. The error control was less than 0.02%, which can meet the requirement. Moreover, the performance of the system was evaluated to analyze how the distortion affects the ability of the system.

In the evaluation of the operating range of an infrared imaging detection system, the average atmospheric transmittance is used, which is also a function of the operating range. Therefore, the operating range must be calculated using iteration algorithm. A variable step length method for the operating range evaluation of point target detection is introduced. MODTRAN software is used to calculate the atmospheric average transmittance and sky background radiance. The number of pixels, signal-to-noise ratio, and modulation contrast under the set distance are calculated using the evaluation model to identify whether the minimum performance index required for target detection is satisfied, and consequently determine the maximum operating range. When the difference between the set value and real operating range is large, a large step length is adopted; when the difference is small, a small step length is used. Compared with the const step length method, the proposed algorithm can accelerate the calculation significantly while maintaining accuracy.

The F-number and focal depth of infrared monitoring zoom lens are relatively small; therefore, the temperature change can easily cause defocusing, resulting in the deterioration of imaging quality. This article is aimed at the technical requirements for the clear imaging of the fire monitoring infrared zoom lens in the range of －40℃ to 50℃. The finite element method is used to analyze the rigid body displacement and rotation of the front and rear mirrors of each lens under temperature changes and import the rigid body displacement into the Sigit optical-mechanical integrated analysis software to simulate the defocus of the lens under temperature change conditions. The analysis result shows that the focal length variable is between -0.16 to 0.4 mm and the focusing amount is 0.108 to 0.188. In response to the above situation, two cams and two sets of actuators are used to control the movement of the zoom group and the compensation group respectively, for realizing the active athermalization design of the lens to ensure that the imaging is still clear under the condition of temperature change. Finally, the temperature adaptability of the lens optical resolution is evaluated through a temperature reliability experiment. The experimental results show that the spatial resolution is greater than 30 lp/mm during temperature change. The image quality is basically clear during zooming process.

This study proposes an infrared image enhancement method combined with single scale Retinex and guided image filtering to eliminate the problems of low image contrast, loss of detail information, and excessive enhancement in traditional infrared image enhancement algorithms. First, a structure extraction algorithm is used to obtain the incident and reflected components of the original image according to the Retinex algorithm. The platform histogram is used to enhance the contrast of the incident component. Then, the reflected component is decomposed into the base layer and detail layer by the weighted guided image filter based on variance and by performing contrast and detail enhancement operations on the images of the two components, respectively. Finally, the results of each level are fused according to the appropriate weight factors to obtain an enhanced infrared image. The experiments in this study show that the proposed method can improve the overall contrast of infrared images and highlight their detailed features more effectively than other enhancement algorithms. The information entropy and average gradient of the three groups of images after enhancement are 9.7373 and 5.6922, respectively, which are 2.7499 and 3.8296 higher than the original image.

Infrared thermal wave non-destructive testing is a new type of technology that has developed rapidly in the past two decades and is widely used in many fields. However, owing to its vulnerability to influence from environmental factors and the particularity of its uneven working components, there is always a certain degree of noise pollution in the original thermal image of uncooled thermal imaging cameras; therefore, denoising the original thermal image is a key step in this technology. The traditional improved wavelet threshold denoising method is limited to the transformation of the adaptive decomposition scale of the threshold, such that the threshold function is smooth and continuous. There is no targeted method for noise variance estimation, which is the key variable of the threshold that determines the effect of wavelet threshold denoising. This study establishes a mixed noise model based on the noise characteristics of infrared images, estimates the noise variance, improves the threshold and threshold function based on the noise model, obtains the best function parameters through software, and finally analyzes the simulation results, process, and evaluation of real images. The results show that the improved wavelet threshold denoising method has a better denoising effect than the traditional threshold denoising method and partial filter denoising method.

Aiming at the complex problem of thermal fault feature extraction and digital representation in the infrared image diagnosis of power equipment, a multi-attribute fusion thermal feature digitization method for power equipment is proposed in this study. The method uses heat power equipment fault features and diagnostic files related to research analysis, based on image preprocessing, to extract the images of key areas with high temperatures, heating area, location, and thermal property values, such as hot clustering, building a multiple-attribute information fusion of overheating fault feature vectors to realize a digital description of the thermal fault characteristics. A circuit breaker is used as an example to verify and analyze the proposed method. The results show that the proposed method can effectively describe the typical infrared fault spectrum, and can be used in the intelligent classification and diagnosis of equipment faults in the case of a large number of complex fault samples.

A large number of infrared images are generated during the operation of power equipment. When the power equipment in the infrared image is densely arranged, incline-angled, and has a large aspect ratio, the target detection network based on a horizontal rectangular frame can only provide the approximate posi-tion of the target, which is prone to overlap with the target detection area and introduce redundant back-ground information, giving detection results that are not sufficiently accurate. To solve this problem, we propose to introduce a rotating rectangular box mechanism into the retina net target detection network and mosaic data enhancement technology at the network input, replacing the ReLU function in the original backbone network with a smoother mish activation function of gradient flow; the Pan module is added after the FPN module of the original model to further fuse image features. Finally, the data set is made by using the power-equipment infrared images collected on-site. The improved model is compared and evaluated with three target detection networks based on horizontal rectangular frame positioning: fast R-CNN, YOLOv3, and original RetinaNet. The experiments show that the improved model can detect the infrared targets of power equipment with inclination in dense scenes more accurately, and the detection accuracy of mul-ti-category power equipment is higher than that of the above three models.

Overheat fault atlas identification of power equipment is an important tool for judging the fault degree of power equipment. In engineering practice, thermal photography of substation equipment and manual screening are usually carried out. To improve the heat equipment’s figure fault decision accuracy and efficiency according to the characteristics of the three-phase power, this study proposes an automatic search determination method; this method involves the use of the three-phase partition piece temperature contrast of overheating area to separate every figure of the three-phase equipment heat, adjustment of images of similar size and attitude, comparing the new image block, and determining whether there is a corresponding block of abnormal temperature rise to identify the thermal faults of certain phase equipment. The test results show that the equipment thermal diagnostic method based on computer automatic search and determination can identify the thermal fault of three-phase equipment more efficiently and accurately, which improves the efficiency, accuracy, and automation degree of thermal fault detection of power equipment.

The concrete-filled steel tube structure has void defects owing to factors such as construction technology, concrete shrinkage, and overload bearing, which reduce the load-bearing capacity of the structure. Furthermore, the current thermal imaging based void detection technology has a small detection depth, poor processing algorithm rendering effect, inability to detect quantitatively, and low detection efficiency. In this study, an active thermal imaging technology was used to study the void defects of concrete-filled steel tubes. A special induction heating power supply and heating probe are designed to increase the detection depth, and a heat source inversion algorithm eliminates the heating non-uniformity interference. In addition, a defect-free simulation model is established to predict the temperature distribution of the concrete-filled steel tube surface. Moreover, the difference between the experimental data and the simulation prediction data was obtained to extract the characteristics of the void defect and optimize the detection parameters to realize a quantitative detection solution. Through repeated experiments, it was shown that void defects in concrete-filled steel tubes with a wall thickness of 20mm can be detected, and the shape and size of the defects can be determined, which effectively improves the inspection depth and efficiency.

Infrared image intelligent analysis is an effective method for the fault diagnosis of transformer equipment, and its key technology is target device segmentation. In this study, aiming to address the difficulty in overall segmentation of current transformers with complex backgrounds, the DeepLabv3+ neural network based on ResNet50 was applied to train the semantic segmentation model with infrared image of CT. The collected samples were enhanced by the limited contrast adaptive histogram equalization method, and a sample dataset was constructed. The sample dataset was expanded by image distortion, and a semantic segmentation network was built to train the semantic segmentation model to realize the binary classification of current transformer pixels and background pixels. The test results of 420 current transformer infrared images showed that the MIOU of this method is 87.5%, which can accurately divide the current transformer equipment from the test images and lay a foundation for the subsequent intelligent fault diagnosis of current transformers.

In this study, the wall thickness of the desulfurization tower during operation was measured using the conjugate gradient method according to the infrared thermogram of the tower surface. Because of the harsh detection environment inside the desulfurization tower, the inner-wall heat flow, which was needed for wall thickness detection, was also obtained by inversion of the conjugate gradient method. First, the feasibility of the proposed method was verified using numerical experiments. Subsequently, based on the infrared thermal image inversion, it was found that the desulfurization tower had some problems, such as the corrosion of the cylinder, the falling off of the anticorrosive coating, and the thinning. In the subsequent downtime maintenance, the problem area was rechecked, and the anomalies mentioned above were confirmed, which indicated the effectiveness and accuracy of the quantitative detection method for the wall thickness of the desulfurization tower based on the surface infrared thermal image proposed in this study.