Infrared thermography is a new NDT technology with outstanding advantages such as non-contact, large detection area and intuitive detection results, and it has been widely used in NDT and evaluation of metal, non-metal, fiber reinforced polymer (FRP) and thermal barrier coatings. In this paper, the basic principle of infrared thermography technology and the composition of detection system are briefly introduced, especially the characteristics and advantages and disadvantages of optical, ultrasonic, and electromagnetic thermal excitation forms are compared. Then, according to the development history of thermal excitation forms, the research status and progress of optical excitation infrared thermography technology in the non-destructive testing and evaluation of FRP composites and thermal barrier coatings are introduced in detail, focusing on the hot and difficult problems in the non-destructive testing of FRP composites/thermal barrier coatings. Finally, the future development trend of infrared thermographic NDT technology for FRP composites/thermal barrier coatings is summarized and prospected.

Ultrasonic infrared thermography is a nondestructive testing method with significant research value and has the advantages of selective heating and detection of complex workpiece cracks. We introduce the principles and system composition of ultrasonic infrared thermography technology, and its development history and current situation in China is reviewed and summarized. The status of simulation research, composite material damage, fatigue crack, metal component crack, and concrete parts crack in application fields is discussed in detail. Finally, future development trends in ultrasonic infrared thermography technology are discussed.

Subsurface defects in coated steel structures, such as corrosion, steel matrix cracks, and coating debonding, affect the overall structural performance and accelerate the degradation of coating systems. Therefore, this study proposes a YOLO v5-based intelligent detection method for pulsed eddy current thermography of subsurface defects in coated steel structures. This method can automatically detect subsurface defects in coated steel structures without removing the coating, which is of significant importance for engineering applications. The proposed method intelligently detects subsurface defects such as corrosion, cracks, and debonding in coated steel structures without removing the coating. The detection results show that the proposed method can accurately identify and classify four types of subsurface defects in coated steel structures: cracks in the steel matrix, debonding, severe quality loss (corrosion pits and corrosion abrasion), and slight quality loss (thin corrosion layers); the four defect types can be detected with accuracies of 96%, 97%, 95%, and 93%, respectively, while meeting real-time inspection requirements.

Line laser scanning thermography is a nondestructive testing technology that uses a line laser as a thermal excitation source and adopts a scanning heating method. It has unique advantages in the nondestructive testing of carbon fiber composites. Here, three parameters that may affect the detection, namely, scanning direction, scanning speed, and laser power, were identified by analyzing the line laser scanning thermography technique and characteristics of composite materials. A simulation model for the detection of composite materials using line laser scanning was established, and the maximum temperature difference between the center point of the defect surface and surface temperature of the defect-free area was selected as the characteristic quantity for detection. The influence of the above parameters on detection was analyzed, and the relationship between the laser power, scanning speed, and detection was fitted. Based on this, the principle of parameter selection considering detection efficiency during the experiment is summarized.

Infrared thermal imaging technology captures the external radiation intensity of an object, and the obtained thermal image includes not only the outline of the object but also the surface temperature field distribution, which can be visually characterized. Infrared thermal imaging can be combined with image processing technologies to enhance rolling bearing state discrimination. Here, we first introduce the basic principle of infrared thermal imaging technology. Second, different technical methods used in different aspects of rolling bearing condition monitoring and fault diagnosis using infrared thermal imaging technology are summarized and discussed. Finally, we summarize and analyze the advantages, shortcomings, and limitations of the different methods used in infrared thermal imaging fault diagnosis and condition monitoring of rolling bearings. The development prospects of infrared thermal imaging fault diagnosis and condition monitoring of rolling bearings are also summarized and discussed.

Explosion-proof sheets are key components in ensuring the safety of lithium batteries. These aluminum sheets easily cause welding perforations, broken welding, and false welding during the welding process using laser welding technology. Presently, inspection mainly relies on visual inspection and visible light image detection; however, these detection methods cannot detect faulty welding. Here, we propose a pulsed infrared thermography technology to detect laser welding defects. Four specimens with welding ratios of 100, 50, 33, and 25% were fabricated. The signal reconstruction method eliminates the influence of the uneven surface of the specimen. It is shown that the larger the welding percentage, the larger the bright spot in 1D images of the welding area. The binary segmentation of the 1-D image indicated that the laser welding percentage and detection area have a positive linear correlation, which verifies the effectiveness of the pulsed infrared thermal imaging technology for lithium battery explosion-proof sheet welding quality inspection, providing a new method for detecting the welding quality of lithium battery explosion-proof sheets.

Defects in the paste structure inside a workpiece act as a crucial factor affecting production quality and operational safety, and infrared non-destructive testing can be used to detect and evaluate defects. In this study, the blind frequency of the paste structure at different defect depths and thermal diffusivity of the coating were measured through simulations, and the influence of the defect depth and thermal diffusivity of the coating on the blind frequency was studied. Relationship between the defect depth and thermal diffusivity of the coatings. The simulation results show that the thermal diffusion length can be obtained using the blind frequency, and a quantitative detection method for the defect depth can be obtained.

With the increasing exploration, development, and utilization of China's oceans, rivers, and groundwater resources, as well as the increasingly urgent military need for sovereign defense in territorial waters, obtaining high-quality target images under long-distance underwater conditions has become an urgent problem to be solved in many fields, such as underwater environmental surveys, target detection, and enemy-self confrontation. Currently, the underwater imaging detection technology includes two main methods: acoustic and photoelectric detection. In this study, the current status of the main underwater high-resolution photoelectric detection imaging technology is studied, the advantages and disadvantages of different technical approaches are analyzed, and the photodetectors used in various underwater detection/imaging systems are compared. Combined with its own technical background, it is proposed that the development of a GaAsP photocathode dual microchannel plate image intensifier with high sensitivity, low noise, high gain, fast response, wide dynamic range, and good linearity should be accelerated to simplify the low sensitivity, high noise, low gain, and long processing time, owing to the poor performance of the detector in the photoelectric system, and accelerate the conversion speed of various new technologies into products and practical equipment. The results of this study support the development of underwater photoelectric imaging technology.

Optical structure–thermal simulation analysis is an effective method for predicting optical properties and optimizing optical systems. In this study, a multi-physical field coupling modeling method based on the COMSOL multiphysics finite element analysis software, coupled with heat transfer, solid mechanics, and geometric optics, is proposed. Compared with the traditional structure-thermal-optical property simulation analysis method, this method does not require surface fitting for optical lenses or data transmission through multiple software, improving the efficiency and accuracy of the simulation. A multi-physical field-coupling simulation analysis model is constructed for an off-axis four-mirror optical system. The structural and optical mirror deformations of the optical system under different temperature conditions are analyzed, and the optical performance changes are determined using ray tracing and spot diagrams, resulting in effective optimization of the optical system.

Herein, the design and implementation of a high-precision photoelectric ship target positioning system is introduced to obtain the absolute position and elevation of a moving ship target for real-time measurements in places such as sea-crossing bridges or coastal airports. By integrating high-definition and high-precision optical detection components into the high-definition PTZ equipment, the absolute position and altitude information of the target point can be calculated in real time and reported to the monitoring system. Combined with the guidance of a radar system, it can automatically measure the longitude, latitude coordinates, and altitude of passing ships under unattended conditions. An actual operational test showed that the system had good reliability, measurement accuracy, and application prospects.

A new generative adversarial network method is proposed for colorization of near-infrared (NIR) images, because current convolutional neural networks fail to fully extract the shallow feature information of images. This failure leads to miscoloring of the local area of the resultant image and blurring due to unstable network training. First, a self-designed dilated global attention module was introduced into the generator residual block to identify each position of the NIR image accurately and improve the local region miscoloring problem. Second, in the discriminative network, the batch normalization layer was replaced with a gradient normalization layer to enhance the network discriminative performance and improve the blurring problem caused by the colorized image generation process. Finally, the algorithms used in this study are compared qualitatively and quantitatively using the RGB_NIR dataset. Experiments show that the proposed algorithm can fully extract the shallow information features of NIR images and improve the structural similarity by 0.044, PSNR by 0.835, and LPILS by 0.021 compared to other colorization algorithms.

To accurately identify a single infrared or visible image under all weather conditions, a gradient image fusion model is proposed to fuse infrared and visible images. First, the accelerated up robust features algorithm is used to match the feature points of the two images. Further, the sampled shear wave transform (NSST) algorithm decomposes the image to be fused to form a map with high-frequency and low-frequency component information and then fuses the high-frequency and low-frequency component maps of insulators to achieve local fusion. The high- and low-frequency component maps are inversely transformed by the inverse transform of the NSST to obtain the final fusion map and achieve global fusion. The quality of the fused images is also evaluated. The line fitting algorithm based on the least-squares method is used to detect insulator self-explosions based on a binary image, the pixel integral projection method is used to detect cracks in the insulator, and color features are used to detect whether the insulator surface is polluted. The accuracies of the detection results of a single image and fusion image were compared through experiments. The experimental results show that the recognition rates of insulator self-explosion, insulator cracks, and insulator surface contamination based on fusion images are 95%, 91%, and 90%, respectively, which are higher than the recognition rates of single infrared image or visible image.

Infrared images of sulfur fluoride (SF6) leaked gas in power equipment are easily merged with the background, and it is difficult for humans to identify the leaked gas in low-contrast images, rendering it difficult to maintain power equipment. A low-contrast enhancement method for SF6 leaked infrared image based on tri-histogram equalization is proposed. First, a cubic spline interpolation is used to fit the image histogram to construct a second-order continuous curve, and the absolute value of the first derivative of the curve corresponding to each gray level is calculated. Second, the two extreme points of the histogram are calculated according to the absolute value distribution of the first derivative and histogram. The extreme points divided the histogram into two peaks and relatively flat valley regions. Finally, the image is divided into three sub-images according to the tri-histogram, and the three sub-images are histogram-equalized. The sub-images are merged into a new image, which is the enhanced image. To verify the validity of our algorithm, an infrared video of power equipment with SF6 leakage, which is recorded in substation fields, is tested to enhance the contrast. Our algorithm is compared with the CLAHE and bi-histogram equalization algorithms. The experimental results show that our method enhances not only the globe contrast for infrared images but also the local contrast for leaking gas. Therefore, the infrared image of the SF6 visual effect is improved. Compared with CLAHE and the bi-histogram equalization method, our method obtained a higher peak signal-to-noise ratio and root-mean-square contrast values, and the enhanced image quality of our method was better than that of the other methods.

The brightness gain and maximum output brightness of super gen Ⅱ auto-gated image intensifiers vary with temperature. Here, we analyzed the principles of temperature compensation and designed a temperature compensation scheme. The compensation coefficient was determined experimentally, and the rationality of the temperature-compensation scheme was verified using the data. The experimental results showed that the low temperature (-45℃) brightness gain can be reduced from 121% to 105% by reducing the MCP voltage by 14.7 V under low illumination conditions (input illumination is less than 5.10-4 lx), and the high temperature (55℃) brightness gain can be increased from 77% to 99% by increasing the MCP voltage by 16.5 V. Under high illumination conditions (input illumination of more than 5.10-4 lx), the maximum output brightness at low temperatures can be reduced from 114% to less than 104% by reducing the anode current setting value by 14%, and the maximum output brightness at high temperature can be increased from 87% to more than 91% by increasing the anode current setting value by 12.6%. Therefore, the temperature compensation technology described herein can effectively improve the consistency of the brightness gain and maximum output brightness of auto-gated image intensifiers under high- and low-temperature conditions.