Application of Spaceborne High‑Sensitivity Short‑Wave Infrared Imaging Technology (Invited)

Advances in imaging technology have huge impact on our daily lives. Innovations in optics, focal plane arrays (FPA), microelectronics and computation have revolutionized camera design. As a result, new approaches to camera design and low cost manufacturing is now possible. These advances are clearly evident in visible wavelength band due to pixel scaling, improvements in silicon material and CMOS technology. CMOS cameras are available in cell phones and many other consumer products. Advances in infrared imaging technology have been slow due to market volume and many technological barriers in detector materials, optics and fundamental limits imposed by the scaling laws of optics. There is of course much room for improvements in both, visible and infrared imaging technology. This paper highlights various technology development projects at DARPA to advance the imaging technology for both, visible and infrared. Challenges and potentials solutions are highlighted in areas related to wide field-of-view camera design, small pitch pixel, broadband and multiband detectors and focal plane arrays.

Efficient facial expression recognition via convolution neural network and infrared imaging technology

Astro-Ecology couples ‘off the shelf’ infrared imaging technology and astronomy instrumentation techniques for application in the field of conservation biology. Microbolometers are uncooled, infrared systems that image in the thermal-infrared range (8-15μm). These cameras are potentially ideal to use for the detection and monitoring of vulnerable species and are readily available as ’off the shelf’ systems. However to optimise the quality of the data for this purpose requires thorough detector calibration to account for the systematics that limit readout accuracy. In this paper we apply three analogous, standard astronomical instrumentation techniques to characterise the random and spatial noise present in a FLIR Tau 2 Core thermal-infrared camera. We use flat fielding, stacking and binning to determine that microbolometer FPAs are dominated by large structure noise and demonstrate how this can be corrected by subtracting median stacks of flat field exposures.

To summarize development processes and research hotspots of infrared imaging technology research on acupuncture and to provide new insights for researchers in future studies. Publications regarding infrared imaging technology in acupuncture from 2008 to 2023 were downloaded from the Web of Science Core Collection (WoSCC). VOSviewer 1.6.19, CiteSpace 6.2.R4, Scimago Graphica, and Microsoft Excel software were used for bibliometric analyses. The main analyses include collaboration analyses between countries, institutions, authors, and journals, as well as analyses on keywords and references. A total of 346 publications were retrieved from 2008 to 2023. The quantity of yearly publications increased steadily, with some fluctuations over the past 15 years. "Evidence-Based Complementary and Alternative Medicine" and "American Journal of Chinese Medicine" were the top-cited journals in frequency and centrality. China has the largest number of publications, with the Shanghai University of Traditional Chinese Medicine being the most prolific institution. Among authors, Litscher Gerhard from Austria (currently Swiss University of Traditional Chinese Medicine, Switzerland) in Europe, was the most published and most cited author. The article published by Rojas RF was the most discussed among the cited references. Common keywords included "Acupuncture," "Near infrared spectroscopy," and "Temperature," among others. Explore the relationship between acupoints and temperature through infrared thermography technology (IRT), evaluate pain objectively by functional near-infrared spectroscopy (fNIRS), and explore acupuncture for functional connectivity between brain regions were the hotspots and frontier trends in this field. This study is the first to use bibliometric methods to explore the hotspots and cutting-edge issues in the application of infrared imaging technology in the field of acupuncture. It offers a fresh perspective on infrared imaging technology research on acupuncture and gives scholars useful data to determine the field's hotspots, present state of affairs, and frontier trends.

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature.

The measurement of muscle oxygenation levels by near infrared spectroscopy imaging technology is hindered by light scattering and absorption in tissues. This leads to a limited measurement range and necessitates a significant amount of time for optical signal acquisition. Therefore, this article used photosensitive π-conjugated materials for measurement optimization in near infrared spectroscopy imaging technology. Firstly, photosensitive π-conjugated materials were applied to near infrared spectrometers for spectral measurements. Secondly, the elimination of uninformative variables and the ratio of regression coefficients to spectral residuals were used for wavelength screening. Subsequently, the spectral data was preprocessed, and principal component analysis was used for quantitative correction. Finally, the effectiveness of near infrared spectroscopy imaging technology optimized using photosensitive π-conjugated materials was verified through experiments. In terms of measurement range, the near infrared spectrometer optimized using photosensitive π-conjugated materials expanded the measurement range by 42.7%; in terms of optical signal acquisition time and measurement accuracy, the acquisition time of near infrared spectrometers optimized with photosensitive π-conjugated materials was shorter than that of near infrared spectrometers optimized without photosensitive π-conjugated materials. In terms of measurement accuracy, the near infrared spectrometer optimized using photosensitive π-conjugated materials had higher accuracy, both exceeding 98%. The use of photosensitive π-conjugated materials in near infrared spectral imaging analysis had good monitoring effects, and could quickly, accurately, and comprehensively measure muscle oxygenation levels, making it very suitable for application in sports.

Infrared thermography (IRT) imaging technology, as a convenient, efficient, and contactless temperature measurement technology, has been widely applied to animal production. In this review, we systematically summarized the principles and influencing parameters of IRT imaging technology. In addition, we also summed up recent advances of IRT imaging technology in monitoring the temperature of animal surfaces and core anatomical areas, diagnosing early disease and inflammation, monitoring animal stress levels, identifying estrus and ovulation, and diagnosing pregnancy and animal welfare. Finally, we made prospective forecast for future research directions, offering more theoretical references for related research in this field.

<div class="htmlview paragraph">Since it's inception, infrared (IR) imaging technology has demonstrated nearly limitless applications in situations where surface temperature data are required. IR imaging systems offer numerous advantages over conventional surface temperature measurement techniques at the expense of a relatively large financial investment. Nevertheless, IR imaging systems are valuable engineering tools and are well suited for use in the development and evaluation of vehicular heat exchangers. The purpose of this paper is to provide background and fundamental information on IR imaging technology and to discuss it's application to heat exchanger development. Finally, several basic application examples on the use of IR imaging technology in heat exchanger development will be presented.</div>

Target detection technology is one of the basic topics in the field of computer vision, and it is also a hot spot, with a very broad application market. However, most of the current target detection technologies based on deep learning are aimed at visible light imaging technology, and there are very few researches on infrared imaging technology. Target detection based on deep learning implements the learning of more features by abstracting, extracting, processing and integrating the essential features of a large number of samples. Therefore, the use of deep learning target detection algorithms for infrared image pedestrian detection applications can make up for the shortcomings of traditional detection methods. YOLOv3 is currently a relatively balanced recognition algorithm. This article analyzes the principles and characteristics of the YOLOv3 series of algorithms to optimize multi-scale detection, which improves the detection accuracy and achieves a relative balance between detection accuracy and speed to a certain extent. This research hopes to provide efficient and feasible solutions and solutions for infrared target detection and recognition in the air through the application of deep learning technology.

Infrared Imaging Technology: Threatening to See Through the Fourth Amendment

To extract the license plate information in night scene and improve the vehicle safety management competence, the research extracts the license plate message of night driving vehicles based on the infrared image technology. The license plate number is located by using the log operator edge detection, and characters are segmented and analyzed for realizing the function that vehicle license plate information acquirement. The result of the research indicates that infrared thermal imaging technology is able to pick up the plate information at night, which provides the reference for night plate information detection.

Existing FOD detection systems use radar or visible imaging equipment to scan and detect airport runway FOD. With the continuous emergence of new technologies, it is necessary to research the application of new technologies in the field of FOD detection. By analyzing the characteristics of new technologies such as infrared imaging technology, ultra-high-resolution imaging technology and drones, we focus on their advantages over existing technologies and put forward the overall design ideas and the key technology for the application of each new technology in the field of FOD detection. Combining various new technologies with existing FOD detection technologies will effectively expand FOD detection methods.

In recent years, infrared imaging technology has developed rapidly and has been increasingly used in military reconnaissance, security surveillance, and medical imaging. However, in the process of infrared image imaging or transmission, it is affected by many factors such as environment and equipment. The infrared image often has a low resolution, which greatly reduces the amount of information contained in the infrared image and restricts the application value of the infrared image. Therefore, how to obtain high-resolution and high-information infrared images has become an issue that people urgently need to solve. In recent years, the development of deep learning technology has made rapid progress, and super-resolution methods based on deep learning have begun to appear. However, if A super-resolution reconstruction method of infrared images based on channel attention and transfer learning was proposed to solve the problems of low resolution and low quality of infrared images. In this method, a deep convolutional neural network is designed to enhance the learning ability of the network by introducing the channel attention mechanism, and the residual learning method is used to mitigate the problem of gradient explosion or disappearance and to accelerate the convergence of the network. Because high-quality infrared images are difficult to collect and insufficient in number, so this method is divided into two steps: the first step is to use natural images to pre-train the neural network model, and the second step is to use transfer learning knowledge to fine-tune the pre-trained model’s parameters with a small number of high-quality infrared images to make the model better in reconstructing the infrared image. Finally, a multi-scale detail boosting filter is added to improve the visual effect of the reconstructed infrared image. Experiments on Set5 and Set14 datasets as well as infrared images show that the deepening network depth and introducing channel attention mechanism can improve the effect of super-resolution reconstruction, transfer learning can well solve the problem of insufficient number of infrared image samples, and multi-scale detail boosting filter can improve the details and increase the amount of information of the reconstruction image.these convolutional neural networks are directly applied to the infrared image field, there are some problems: SRCNN, FSRCNN, and ESPCN have fewer network convolutional layers and insufficient network depth, and the learning features will be relatively single, ignoring the differences between image features. The mutual relationship makes it difficult to extract the deep-level information of the infrared image, and SRGAN may generate super-resolution images that are not close to the original image in certain details, which is not conducive to the application of infrared images in military, medical and surveillance. Another problem that needs to be overcome is that it is difficult to collect a sufficient number of high-quality infrared images in real life, and a large number of images of different scenes and targets are required as training samples for common deep learning methods. The infrared images used as training data sets to achieve deep learning methods often fail to achieve the desired effect. In order to solve these problems, this paper proposes a method for super-resolution reconstruction of infrared images based on channel attention and transfer learning. This method first designs a deep convolutional neural network, which integrates the channel attention mechanism to learn the correlation between the channels in the feature space, enhances the learning ability of the network, and uses residual learning to reduce the problem of gradient explosion or disappearance and to speed up network convergence. Then, considering that high-quality infrared images are difficult to collect and insufficient in number, the network training is divided into two steps: the first step uses natural images to pre-train a super-resolution model of natural images, and the second step is to use transfer learning knowledge. Using a small number of high-quality infrared images, the pre-trained model pa-rameters are quickly transferred and fine-tuned to improve the reconstruction effect of the model on the infrared image, thereby obtaining a super-resolution model of the infrared image. Finally, a multi-scale detail boosting (MSDB) module is added to enhance the details and visual effects of the infrared reconstructed image and to increase the amount of in-formation.

Range-gated laser active imaging technology is an effective way to image detection and precise tracking of remote, dark, and small targets that overcomes the shortcomings of passive visible or infrared imaging technology, thus has important practical value and broad application prospects in the military. The paper based on the analysis of its principle, technical advantages and key technologies focus on the typical systems under atmospheric conditions at home and abroad and the latest research results, and discusses the development trends of this technology.

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness.