Infrared Detection Method Research Articles

A skin-beyond multifrequency camouflage system with self-adaptive discoloration and radar-infrared stealth

Camouflage technology could enhance the safety and security of desirable targets by concealing their signatures from the microwave, vision, infrared detection methods. However, conventional single-band camouflage fails to align with the current trends toward multi-band compatibility and intelligent adaptation. Here a bioskin-inspired multifrequency camouflaging system, including an artificial multispectral camouflage skin and an adaptive processing unit, was designed to simultaneously achieve infrared, radar, and adaptive visual stealth. This skin adopts a vertically stacked structure and utilizes a square patterned indium tin oxide for infrared stealth, a sector-box nesting silver nanowire-graphene (AgGr) metasurface for radar stealth, and a light-controlled AgGr heater to drive thermochromic displays for visible stealth. The system achieves multiband compatible properties with ultralow infrared emissivity of 0.24, high and broad microwave absorption in the 10 GHz-37.5 GHz region, and highly self-adaptive discoloration in response to background-color changes. Our work provides a reliable all-in-one design in flexible smart optoelectronics for future camouflage skin.

Deep learning-assisted common temperature measurement based on visible light imaging

Abstract Real-time, contact-free temperature monitoring of low to medium range (30°C ~ 150 °C) has been extensively used in industry and agriculture, which is usually realized by costly infrared temperature detection methods. This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network (CNN). A mean square error of ＜ 1.119 °C was reached in the temperature measurements of low to medium range using the CNN and the visible light images. Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN. Moreover, the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training. Compared to the conventional machine learning algorithms mentioned in the recent literatures, this real-time, contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

Sustainable Infrastructure Maintenance: Crack Depth Detection in Tunnel Linings via Natural Temperature Variations and Infrared Imaging

In this research, we propose the use of infrared detection methods for identifying cracks in the tunnel lining of concrete structures. Through thermal simulation experiments on pre-existing cracks, we investigate the heat conduction patterns in cracked linings under natural temperature variations. The influence of temperature differences inside and outside the lining, crack depth, and crack width on the temperature distribution on the inner surface of the lining is analyzed by using a controlled variable approach. This exploration aims to assess the feasibility and applicable conditions of using infrared thermal imaging technology for detecting lining crack defects, contributing to sustainable maintenance of infrastructure. We further validate the experimental approach through numerical simulations. Considering the temperature distribution on the inner surface of the lining, it becomes feasible to comprehensively determine the location and depth of cracks. This offers a novel and rapid inspection method for tunnel lining cracks, thereby enhancing the sustainability of tunnel infrastructure.

Analysis and research on polarized radiation characteristics of ship targets at sea

The traditional ship infrared detection method cannot solve the edge details when the ship target interacts flexibly with the sea surface background. Polarization detection technology has gradually become an important research direction for high-precision detection of marine targets because of its advantages of acquiring the intrinsic characteristics of targets. This paper selects the typical part panels and scaled models of ships as experimental research object, focusing on the analysis of the polarized radiation characteristics of marine ship targets. Starting from the principle of polarization detection, we extended the application of the bidirectional polarization reflection distribution function that can obtain the polarization spectral characteristics of the target. According to the principle of time-sharing polarization detection, we designed a simulation experiment scheme for the polarization characteristics of the typical parts of the ship and the overall model related to the sea surface background. Finally, we implemented the measurement experiment of multi-angle polarization characteristics in the field and performed data analysis. The results show that: (1) The polarization reflectivity and polarization degree of the typical parts of the ship tend to be consistent with the changes of the observation geometric conditions, both increase with the increase of the observation zenith angle, and the forward scattering area is larger than the back scattering area. (2) The observation direction has a great influence on the overall detection results of the ship. When using the 30°zenith angle of the forward scattering area of the main plane for observation, the contrast between the ship and the water body is obvious, and it is easy to detect the ship.

Detecting geothermal anomalies using Landsat 8 thermal infrared remote sensing data in the Ruili Basin, Southwest China.

With the recent increase in global focus on green energy, the application of thermal infrared remote sensing data for the detection of geothermal anomalies has attracted wide attention as it can overcome the difficulty of using only ground surveying. This study aimed to highlight areas of geothermal anomalies with land surface temperature (LST) time series data in winter derived from thermal infrared remote sensing. To extract LST anomaly areas in the Ruili Basin for geothermal prospecting, nine types of data on the study area in winter during 2014 ~ 2021 from Landsat 8 were analyzed. Landsat 8 LST inversion data based on the mono-window algorithm (MWA) can be used to identify hot springs, volcanoes, and other heat-related phenomena. Superimposing LST anomalies for each cycle through drilling data, excluding the heat island effect, geothermal anomaly regions could be plotted. The results show that the accuracy of MWA LST varied within 2K, which is acceptable for geothermal energy and higher than those of the radiative transfer equation (RTE) algorithm and MODIS LST products. Three high-LST regions in the southeast of the study area were identified as geothermal anomaly areas (A, B, and C), and region B was further verified through a comprehensive field investigation of geothermal wells, supplemented by the temperature gradient (TG) method. The findings reveal that the distribution of geothermal anomaly areas and high-LST areas are highly consistent with the northeast trending fault structure; faults act as thermal channels and help in accurately detecting local LST anomalies. Overall, the infrared remote sensing method proved to be a valid technique for detecting LST anomalies. Considering the synergy between thermal infrared surface detection and subsurface exploration methods, the identification of known geothermal fields (B) and other possible areas (A and C) has significance in the upscaling of local geologic information to regional prospecting, thus providing a direction for future geothermal research.

An Intelligent Near-Infrared Diffuse Reflectance Spectroscopy Scheme for the Non-Destructive Testing of the Sugar Content in Cherry Tomato Fruit

As a new non-destructive testing technology, near-infrared spectroscopy has broad application prospects in agriculture, food, and other fields. In this paper, an intelligent near-infrared diffuse reflectance spectroscopy scheme (INIS) for the non-destructive testing of the sugar contents in vegetables and fruits was proposed. The cherry tomato were taken as the research object. The applicable objects and features of the three main methods of near-infrared detection were compared. According to the advantages and disadvantages of the three near infrared (NIR) detection methods, the experiment was carried out. This experiment involved the near-infrared diffuse reflection detection method, and the back propagation (BP) network model was established to research the sugar content of the cherry tomatoes. We used smoothing and a principal component analysis (PCA) to extract the final spectrum from the experimental spectrum. Taking the preprocessed spectral data as the input of the network and the measured sugar content of the cherry tomatoes as the output, the 80-12-1 network model structure was established. The cross-validation coefficient of determination was 0.8328 and the mean absolute deviation was 0.5711. The results indicate that the BP neural network can quickly and effectively detect the sugar content in cherry tomatoes. This intelligent near-infrared diffuse reflectance spectroscopy (INIS) scheme can be extended and optimized for almost all sugar-containing fruits and vegetables in the future.

Research and Improvement of SBS Asphalt-Modifier Content Testing Technology Based on Infrared Spectrum

Infrared spectroscopy SBS asphalt-modifier content detection technology has been applied in practical projects, which can effectively ensure the quality of pavement engineering. If the composition ratio of modified asphalt is not clear, the accuracy of the test results will be affected, which is the disadvantage of conventional infrared spectroscopy detection methods. In this paper, the influence of different kinds of additives on the accuracy of the infrared spectrogram and SBS content test was analyzed, and the microscopic mechanism of the effect of additives on the accuracy of SBS modified asphalt content testing was uncovered. Meanwhile, the sample preparation technique was improved and the testing technique was optimized. Results demonstrate that the improved standard curve algorithm eliminates the effect of additives on test accuracy, and the effect of additives on test accuracy was eliminated by improving the standard curve algorithm. Without the addition of additives, the content of the modifier is accurately measured by simplifying the boundary conditions of the test technique. The preparation of samples by the dissolution method has more advantages than the tableting method and can be used as a new sample preparation technology. Improvements in sample preparation techniques and standard curve algorithms improve the safety, convenience, applicability, and accuracy of detection techniques.

Preliminary assessment of the impact of soil microorganisms on greenhouse gas emissions expressed in CO 2 equivalent and grass biomass

The pot experiment was conducted to access the soil microorganisms biomass (physiological method – Substrate Induced Respiration) and emissions of N2O, CO2 and CH4 (photoacoustic infrared detection method), and grasses biomass (weight method). The obtained results of analysed gases were converted to CO2 equivalent. There was no effect of the microorganisms biomass on the N2O emissions. The increase in CO2 emissions was accompanied by an increase in the microorganisms biomass (r = 0.48) under the conditions of the I swath and acid soil reaction, as well as the II swath and neutral reaction ( r = 0.94). On the other hand, in the case of CH4 emission, such a relationship was noted both swaths under the conditions of neutral reaction ( r = 0.51), but a negative correlation ( r = –0.71) was noted for the acid reaction only at the II swath. The increase in the grasses biomass with the increase in the microorganisms biomass was recorded only at the II swath in neutral reaction ( r = 0.91). In a short period of time, with the neutral soil reaction with the increase in the soil microorganisms biomass, an increase in CO2 sequestration and biomass of cultivated grasses was noted. Information on the determination of the microorganisms groups responsible mainly for the transformation of carbon compounds and CO2 and CH4 emissions from the soils of grasslands would be valuable scientifically.

Analysis of the detection method of insulators deterioration based on optical electric field sensors

With the acceleration of uh-voltage transmission project construction, transmission lines are becoming increasingly important to maintain the safe and stable operation of the power grid, and insulators are essential components of mechanical support and electrical connection in transmission lines. However, due to long-term environmental impact, insulators exposed deteriorate year by year. Therefore, it is necessary to detect the faulty insulator in advance, and to carry out the test in the operating state of the insulator string to identify and replace the faulty insulator string in time. However, the current method of detecting insulators in actual engineering is greatly affected by the environment, the test result is not accurate enough, the predictability is not good and the detection efficiency is low. The optical electric field sensor based on the Pockels effect of optical crystals can realize chargerd detection, improve detection accuracy, reduce environmental impact, and make the detection more stable, lower cost, better economy compared with methods widely used in the market, such as infrared detection method, ultraviolet detection method, sonic detection method, manual methods. Further consideration can be given to combining with drones to expand usage scenarios and improve detection performance for widespread use.

Infrared Infusion Monitor Based on Data Dimensionality Reduction and Logistics Classifier

This paper presents an infrared infusion monitoring method based on data dimensionality reduction and a logistics classifier. In today’s social environment, nurses with hospital infusion work are under excessive pressure. In order to improve the information level of the traditional medical process, hospitals have introduced a variety of infusion monitoring devices. The current infusion monitoring equipment mainly adopts the detection method of infrared liquid drop detection to realize non-contact measurements. However, a large number of experiments have found that the traditional infrared detection method has the problems of low voltage signal amplitude variation and low signal-to-noise ratio (SNR). Conventional threshold judgment or signal shaping cannot accurately judge whether droplets exist or not, and complex signal processing circuits can greatly increase the cost and power consumption of equipment. In order to solve these problems, this paper proposes a method for the accurate measurement of droplets without increasing the cost, that is, a method combining data drop and a logistics classifier. The dimensionalized data and time information are input into the logistics classifier to judge the drop landing. The test results show that this method can significantly improve the accuracy of droplet judgment without increasing the hardware cost.

Surface Roughness Detection of Roof Insulator Based on Hyperspectral Technology

The strong airflow generated during the operation of the high-speed train will carry sand and dust, causing high-speed sand and dust to hit the surface of the external equipment of the vehicle body, which will increase the surface roughness of the roof insulators, resulting in the changes of the hydrophobicity, fouling characteristics and insulation performance of the insulator. It poses a hidden danger to the safe and stable operation of the train. In our existing surface roughness testing methods, contact detections and optical stylus methods can cause damage to the insulator surface. Infrared detection methods will be disturbed by leakage current. Most methods need to be disassembled for testing. These methods have certain disadvantages. Therefore, this paper proposes a non-contact detection method for the surface roughness of the roof insulators. Firstly, we extract the image information of the insulator surface by hyperspectral imager, then preprocess the extracted hyperspectral image and use the continuous projection algorithm to reduce the data. Finally, we use the support vector machine to construct the insulator surface roughness discriminant model, and successfully realize the hyperspectral detection method of the surface roughness of the roof insulators, and verify the effectiveness of the proposed method by experiments. As a non-contact detection method, this method can detect the surface roughness of the roof insulators in the non-disassembly conditions, and help the field staffs to grasp the surface roughness of the high-speed train roof insulators in time.

Experimental reproduction of the martian weathering profiles argues for a dense Noachian CO2 atmosphere

On Mars, mineral sequences have been detected and they are composed of a top layer of Al-rich clay minerals, then (Al, Fe)-rich clay minerals and a bottom layer composed of (Mg, Fe)-rich clay minerals. By analogy with Earth, such sequences are interpreted as weathering profiles formed by the interaction of acidic solutions in equilibrium with the atmosphere and the parent rock. Thus, understanding of the aqueous solution composition leading to the above mineral description allows deciphering the atmosphere composition. We designed an experimental column system with three levels containing powdered basaltic rock to test the influence of different acidic fluids on the mineralogical formation. Five solutions were used: H2SO4 and HCl at pH 3 in equilibrium with N2 atmosphere, pure water in equilibrium with 0.1 and 1 atmospheric pressure CO2 leading to pH values of 3.9 and 4.4, respectively and a H2SO4 solution at pH 3 in equilibrium with 0.1 atmospheric pressure CO2 leading to a pH value of 2.98. The results obtained show that the content of Al-rich clay minerals and the evolution from Al, (Al, Fe) to (Fe, Mg)-rich clay minerals formed are better reproduced with an originally high pCO2. Hence, we suggest that acidic alteration driven by a dense CO2 atmosphere reproduced better the observed martian weathering profiles. The experiments involving CO2 led to the formation of carbonates. Their identification by near infrared (NIR) detection methods is challenged, because the laboratory NIR spectra acquired on the experimental products show that: (i) the absorption bands related to carbonates are very weak, and (ii) the strongest feature at 3.95 μm is beyond the CRISM NIR range. Such carbonate formation is consistent with the recent carbonate detection at a planetary scale in weathering profiles, which goes toward that the weathering profiles could have been formed under a dense CO2-rich atmosphere as suggested also by climatic models.

基于Goertzel算法的红外气体检测方法

基于Goertzel算法的红外气体检测方法

A Far-Field Evaluation Method for Interfacial Defects Existed in Composite Insulators Based on Transient Thermal Wave

The interfacial defect of composite insulators is common, which is majorly responsible for the fracture of insulators after the acid resistant core rod is used. Based on the active infrared detection method, the far-field evaluation method can effectively keep the operation safety of transmission lines by detecting subtle internal defects which are difficult to find by the traditional non-destructive detection methods. Above all, the propagation law of heat has been studied. The difference of the surface temperature between defective and reference area has been calculated when the thermal excitation meet the Dirac function. Second, the plate and short insulator samples with interfacial defects have been produced. The defects in plate samples could be easily distinguished by radiation quantity but not in insulators. In order to increase the resolution of experimental result, the derivate of the data has been further processed in the Logarithmic coordinate system. The difference between defective area and reference area has approximately enlarged 7 times and 1 mm defect in insulators, which is the smallest, could be distinguished. The detecting distance has been expanded from several millimeters to 50 centimeters which offers a possibility to the online application. Finally, according to the test results, a preliminary standard for thermal wave evaluation focused on the interfacial defects has been proposed.

Acoustic Localization for a Moving Source Based on Cross Array Azimuth

Acoustic localization for a moving source plays a key role in engineering applications, such as wildlife conservation and health protection. Acoustic detection methods provide an alternative to traditional radar and infrared detection methods. Here, an acoustic locating method of array signal processing based on intersecting azimuth lines of two arrays is introduced. The locating algorithm and the precision simulation of a single array shows that such a single array has good azimuth precision and bad range estimation. Once another array of the same type is added, the moving acoustic source can be located precisely by intersecting azimuth lines. A low-speed vehicle is used as the simulated moving source for the locating experiments. The length selection of short correlation and moving path compensation are studied in the experiments. All results show that the proposed novel method locates the moving sound source with high precision (<5%), while requiring fewer instruments than current methods.

Dual band infrared detection method based on mid-infrared and long infrared vision for conveyor belts longitudinal tear

In mining operation, the longitudinal tear detection of conveyor belt is of great significance to safety production. A novel method, named Dual Band Infrared Detection (DBID), is proposed to detect the longitudinal tear of conveyor belts in this paper. The DBID method is based on the collaboration of mid-infrared and long infrared vision. The DBID sensor device captures the lower surface images of conveyor belt. The feature of tear or scratch and tearing precursor can be extracted by analyzation of captured images. The DBID method is verified by experiments and practice, including the capturing of dual band infrared images (mid-infrared and long infrared images), image preprocessing, and tear detection. The DBID method is not only unlimited by visual condition, but also much more accurate and reliable compared with the Integrative Binocular Vision Detection (IBVD) method presented before which meets the requirement of real-time online detection.

An Optimized Infrared Detection Strategy for Defective Composite Insulators According to the Law of Heat Flux Propagation Considering the Environmental Factors

Composite insulators have been extensively adopted in electric transmission lines due to their preferable resistance to contamination flashover. To generally ensure the function of power networks, it is of crucial importance to perform regular online detection and the timely renewal of poor-quality insulators. As the most extensively employed method for the online analysis of composite insulators, infrared (IR) detection is defective based on the false detection rate and the high rate of undetected inner defects. Via numerical simulation and analysis and a test of the simulation model, this paper seeks to systematically ascertain how the defect temperature and IR results are related to each other for different types of conditional thermal defects in composite insulators, especially the relationship between humidity and heating. Finally, considering our previous findings and the on-site feasibility of the methods, some suggestions for IR detection methods regarding composite insulators are proposed. The findings of this paper are conducive to elucidating the conduction of heat flowthrough the composite insulators and reducing the false detection rate arising from moisture and inner defects. Furthermore, the optimized IR detection method targeting high humidity areas can also be valuable in reference to other power supply units.

Influences of frequency on nitrogen fixation of dielectric barrier discharge in air

The influences of frequency on nitrogen fixation of dielectric barrier discharge in air were studied by electrical diagnostics, gas detection and infrared detection methods. The system power, nitrogen oxide concentration, voltage–current waveform, dielectric surface temperature distribution and filamentous discharge pictures were measured, and then the energy yield was calculated; paper studied their changing tendencies in the presence of frequency. Results show that frequency has strong influences on nitrogen fixation. When the parameters of reaction chamber and amplitude of applied voltage is fixed, with the increasing of frequency, the system power increases; in 5–10 kHz, nitrogen oxide gas concentration up to 1113.7 mg m−3, and 7 kHz is the optimal nitrogen fixation frequency whose energy yield is 20.5 mg (m3 W)−1.

Simulation and Experimental Analysis of the Infrared Detection for Surface Cracks of Metal Pipes with Penetration Treatment

Working in the corrosive environment for a long time, it is easy for metal pipes to produce stress corrosion cracks which will affect the use. An infrared detection method combining permeate treatment with heat-incentive steam is proposed to detect surface cracks, which then has been verified by simulations and experiments. For the simulation, pipe model including four cracks of different depth and width was constructed by ANSYS. Transient thermal analysis was made after convection incentive loaded on internal and external wall in the case of whether or not undergo surface infiltration processing. For the experiment, pipe including cracks were made the same as simulation parameters, then experiments were made using the thermal excitation system in two cases. Surface temperature distributions of the pipe were compared in two cases, the results of the study show that penetration treatment before heat incentive can significantly improve the surface crack detection sensitivity.

Technical Note: Precise quantitative measurements of total dissolved inorganic carbon from small amounts of seawater using a gas chromatographic system

Abstract. Total dissolved inorganic carbon (CT) is one of the most frequently measured parameters used to calculate the partial pressure of carbon dioxide in seawater. Its determination has become increasingly important because of the rising interest in the biological effects of ocean acidification. Coulometric and infrared detection methods are currently favored in order to precisely quantify CT. These methods however are not sufficiently validated for CT measurements of biological experiments manipulating seawater carbonate chemistry with an extended CT measurement range (~1250–2400 μmol kg–1) compared to natural open ocean seawater (~1950–2200 μmol kg−1). The requirement of total sample amounts between 0.1–1 L seawater in the coulometric- and infrared detection methods potentially exclude their use for experiments working with much smaller volumes. Additionally, precise CT analytics become difficult with high amounts of biomass (e.g., phytoplankton cultures) or even impossible in the presence of planktonic calcifiers without sample pre-filtration. Filtration however, can alter CT concentration through gas exchange induced by high pressure. Addressing these problems, we present precise quantification of CT using a small, basic and inexpensive gas chromatograph as a CT analyzer. Our technique is able to provide a repeatability of ±3.1 μmol kg−1, given by the pooled standard deviation over a CT range typically applied in acidification experiments. 200 μL of sample is required to perform the actual CT measurement. The total sample amount needed is 12 mL. Moreover, we show that sample filtration is applicable with only minor alteration of the CT. The method is simple, reliable and with low cumulative material costs. Hence, it is potentially attractive for all researchers experimentally manipulating the seawater carbonate system.