To ensure that researchers are well-informed regarding infrared image edge detection algorithms and to provide a valuable reference for follow-up investigations, we review relevant research conducted on infrared image edge detection algorithms in the past ten years. First, infrared imaging and edge detection technology are summarized, and then, the difficulties and challenges of infrared image edge detection algorithms are described. Finally, the main infrared image edge detection algorithms are summarized, and the related algorithms are divided into four categories: improved classic edge detection operator-based algorithms, ant colony algorithm-based algorithms, mathematical morphology-based algorithms, and network model-based algorithms. Considering traditional infrared image edge detection algorithms, the morphological method has potential because of its simplicity and ease of use; for non-traditional infrared image edge detection algorithms, the method based on deep learning has stronger pertinence, better robustness, and no requirement of designing complex algorithm steps, which brings new development opportunities to infrared image edge detection.

Dynamic range compression of infrared images is an important research direction in the field of infrared image visualization. The dynamic range compression algorithm directly determines the important visualization indexes of the original infrared image, such as detail retention and overall perception; in a sense, it is the basis and guarantee of detail enhancement. This study investigates a wide dynamic range compression algorithm and a local compression algorithm based on a global compression algorithm. Based on the two algorithms, we study and analyze the development process and the advantages and disadvantages to improve the research direction and development trend, which will provide a reference for researchers.

In long-range target detection and tracking, image clarity plays a critical role. An infrared telescope system has a long imaging distance and a short depth of field, and the image blur caused by defocusing tends to be more severe in this system. In addition, because of the atmospheric refraction, the image derived from the telescope constantly changes. This results in a low focusing success rate and low efficiency in traditional focusing algorithms. To improve both the success rate and speed of autofocus, a mountain climbing algorithmic method with a variable step size was proposed in this study. Image clarity was obtained several times, and its median was calculated to ensure image clarity accuracy. Using the mountain climbing algorithm with momentum and acceleration reduces focusing instability as well as the number of steps required for the coarse focusing process. The algorithm was applied in an actual medium-wave infrared telescope system. Experimental results revealed that the focusing steps required by the algorithm for the coarse focusing stage were reduced by 12.8%, in comparison with the traditional mountain climbing method, meeting the requirements of an infrared telescope system.

In this study, a temperature control system was designed based on an infrared thermal image of a hydroelectric hybrid cooling tower. A theoretical model of a cooling tower thermal calculation was deduced based on the structural characteristics of a hydroelectric hybrid cooling tower, as well as the balance equation of heat and mass exchange in a cooling tower and the Merkel mathematical model. A thermal performance monitoring model of a countercurrent cooling tower was first developed using infrared thermal imaging technology, and then the temperature control system for a cooling tower was designed.The causes of temperature control precision errors in the cooling tower temperature control system were analyzed. The accuracy of the thermal calculation model and the feasibility of using a hand-held infrared thermometer for designing the temperature control system of a cooling tower were verified by field experiments.

Slow and inaccurate target detection algorithms used to analyze infrared images are the focus of this study. An infrared image detection method is proposed for substation equipment using an improved Gaussian convolution kernel, which is based on the CenterNet algorithm without an anchor point. In brief, data samples were first collected using on-site substation inspection robot equipment, the algorithm model was trained and verified, and finally, accurate identification and positioning of infrared image substation equipment was achieved. Specifically, based on the infrared image library collected by the substation inspection robot and the infrared thermal imager, methods of deep learning were applied to train and test a model using the dataset, the target detection technology of substation infrared images was studied, and the equipment center was accurately judged through deep learning technology to achieve target classification and regression. The identification and positioning accuracy of the substation target detection were improved by adopting this proposed method, and it provides new ideas for the intelligent detection of infrared images for substation equipment.

A vehicle-based thermal imaging system does not depend on a light source, is insensitive to weather, and has a long detection distance. Automatic target detection using vehicle-based thermal imaging is of great significance for intelligent night driving. Compared with visible images, the infrared images acquired by a vehicle-based thermal imaging system based on existing algorithms have low resolution, and the details of small long-range targets are blurred. Moreover, the real-time algorithm performance required to address the vehicle speed and computing ability of the vehicle-embedded platform should be considered in the vehicle-based thermal imaging target detection method. To solve these problems, an enhanced lightweight infrared target detection network (I-YOLO) for a vehicle-based thermal imaging system is proposed in this study. The network uses a tiny you only look once(Tiny-YOLOV3) infrastructure to extract shallow convolution-layer features according to the characteristics of infrared images to improve the detection of small infrared targets. A single-channel convolutional core was used to reduce the amount of computation. A detection method based on a CenterNet structure is used to reduce the false detection rate and improve the detection speed. The actual test shows that the average detection rate of the I-YOLO target detection network in vehicle-based thermal imaging target detection reached 91%, while the average detection speed was81 fps, and the weight of the training model was96MB, which is suitable for deployment on a vehicle-based embedded system.

Eliminating non-uniformity is a persistent challenge for infrared imaging systems, especially when the integration time varies. This paper describes a non-uniformity correction method with the ability to adapt to arbitrary changes in integration time by correcting the infrared radiation flux map of the scene, which is estimated by pixel-wise radiometric self-calibration. Multiple images of an extended blackbody, obtained with different integration times and blackbody temperatures, were used to obtain the parameters of both the correction model and radiometric calibration model. The correction effect of this method within a wide range of integration times was verified by a high-resolution HgCdTe medium-wave infrared imager.

Existing infrared-guided weapons heavily rely on operators to acquire targets, and the accuracy of acquisition is positively correlated with a target's texture details. To improve the display quality of weak small regions and meet the design requirements of miniaturization, modularization, and low-cost seekers, an image super-resolution(SR) reconstruction algorithm based on a pyramid dense residual network is proposed. The dense residual network is the basic framework of the proposed model. Through the dense connection layer and the residual network, the model can learn the non-linear mapping between images of different scales, and the multi-scale feature can be used to predict the high-frequency residual. In addition, using the deep supervision module to guide network training is conducive to the realization of SR reconstruction with a larger upper-sampling factor and improvements to its generalization ability. A large number of simulation results show that our proposed model outperforms comparison algorithms and that it has a high engineering application value.

In infrared image target detection based on the traditional image segmentation method, when the background color and the color of the detected object are similar, it is often difficult to identify the detected object effectively in the infrared image. Therefore, to further improve the recognition accuracy of insulating bushings in infrared images, this paper proposes a target detection method based on the texture features of insulation bushings. First, to enhance the texture of the image, bilateral filtering is used to replace the Gaussian convolution filtering in the traditional Laplacian of Gaussian, and image filtering and enhancement are performed through Laplace of bilateral filtering. Then, based on the special texture of the outer sheds and insulation bushing, a descriptor reflecting the periodic distribution of sheds was established and rough identification was performed using the image scanning method. Finally, based on the DBSCAN clustering algorithm, a method for solving its hyper parameters was established to achieve outlier elimination and feature clustering, and to complete the fine identification of the high-voltage insulation bushing. By experimentally comparing other recognition algorithms for infrared images of insulating bushings, the algorithm in this study can effectively segment the insulation bushing main body and overcome the shortcomings of traditional image segmentation methods. The recognition rate on the dataset reached over 85%.

To meet the requirements of multiband compatible camouflage, a new composite vegetation camouflage material that can realize multiband compatibility of visible light, infrared, and radar was fabricated by reasonably matching various materials and a multi-functional layer structure. The absorbing properties of the camouflage material were evaluated by a radar wave shielding effectiveness and reflectivity test. The visible and thermal infrared camouflage properties of the camouflage material were tested by an imaging method. The results show that the camouflage material has good radar wave attenuation performance, and the absorption bandwidth at values of 5dB or more is as high as 3.9GHz. The texture, color, brightness, and thermal map of the surface layer of the vegetation camouflage material are close to the background values, and the thermal insulation effect is evident, enabling good visible and infrared camouflage effects.

To study the asymmetric transmission characteristics of the chiral metasurface in the mid-infrared band, a chiral metasurface unit based on an L-shaped structure isdesigned.A simulation analysis using CST electromagnetic software reveals that the asymmetric transmission parameter is greater than 0.8 in the range of 68.92-88.68 THz and reaches the extreme value of 0.88 at 73.25 THz. It can be observedthat the structure exhibits good performance in terms of asymmetric transmission in the mid-infrared band.The polarization selective reflection and cross-polarization transmission mechanism of the chiral metasurface are clarified by analyzing the surface current distribution and phase distribution of the transmission field.The relationship between the chiral strength of the unit structure and the asymmetric transmission characteristics is also discussed.The influence of the thickness of the dielectric and metal layers and the incident angle of the electromagnetic wave on the asymmetric transmission characteristics is examined.

In this study, a transmission terahertz time-domain spectrum system was used to test the terahertz spectra of glycyrrhizic acid, glycyrrhetic acid, and glycyrrhizin as the main components of glycyrrhiza (licorice). The characteristics of these licorice constituents and their terahertz absorption peaks were found to be close to each other and their absorption spectra were similar. A quantum chemistry method was used to simulate the terahertz absorption spectrum of glycyrrhizic acid; this spectrum was then compared with the experimental spectrum to perform a qualitative analysis of the three elements. In this study, based on the density functional theory (DFT) and PM3 models, a single molecular configuration of glycyrrhizic acid was introduced for structural optimization and frequency calculation. The results showed that the terahertz simulated absorption peaks obtained by the two methods coincided with the experimental absorption peaks, and the terahertz simulated absorption spectrum waveforms obtained based on the DFT model were closer to those of the experimental spectrum. Finally, the characteristic absorption peak of glycyrrhizic acid at 1.655THz and the terahertz absorption coefficients of six nearby numerical points were selected, and the average value was used to perform a one-dimensional linear regression fitting with the concentration. The fitting results verified the licorice theoretically, and the acid terahertz absorption spectrum conformed to Lambert's law.

This paper presents a method for radiation measurement and temperature inversion of aircraft skin in flight. Firstly, the skin radiation, atmospheric transport, and absolute radiation correction models are established. Then, the formula of temperature inversion is deduced, and the skin temperature is calculated by successive approximation. Based on theoretical analysis, a wideband long wave (infrared) camera that can image light of wavelength 8-12 μm is used for experimental verification and temperature inversion. By calculating and modifying the uncertainty of inverted temperature, the result of modifying the skin temperature of civil aircraft at 5 km flight height is 268 K; the uncertainty of modifying is 4 K, and the relative uncertainty of modifying is 1.49%.The research work of this paper would be useful for acquiring infrared radiation characteristics of aircraft targets.

When supersonic infrared guided tactical missiles fly, the infrared dome of the missile is affected by the intense aerodynamic heat. The thermal stress generated by heat is the main factor causes thermal cracking of the dome. In response to this problem, in the early development stage of the missile dome, a simple and quick method to select the dome material and whether the next restraint state can be studied is to simulate the actual working state of the solidified missile metal shell of the dome, infrared dome pure thermal stress analysis. The pure heat caused by the temperature gradient ▽_T of the free state dome is separated from the larger heat caused by the superimposed displacement constraint, the smaller pure heat is analyzed separately. The leading factor causing the thermal explosion of the dome is analyzed. Combining the dual-color infrared transmission requirements, taking the zinc sulfide infrared dome as an example, the pure heat simulation is analyzed. The strength limit of ZnS material is greater than pure heat, and the dome can enter the constrained state. In the thermal test the dome did not burst, which proves that this method can be used for the selection of dome materials.