Forward-looking Infrared Images Research Articles

558 Use of Forward-Looking Infrared (FLIR) Technology to Assess Burn Depth and Healing Potential: A Systematic Review and Meta-Analysis

Abstract Introduction Burn wound depth assessments are an important component of determining patient prognosis and making appropriate management decisions. Clinical appraisal of the burn wound by an experienced burn surgeon is standard of care but has limitations. Forward-looking Infrared (FLIR) is a new technology in burn care that can provide a non-invasive, quantitative method of evaluating burn wound depth. FLIR utilizes a specialized camera that can capture the infrared emissivity of the skin, and the resulting images can be analyzed to determine burn depth and healing potential of a burn wound. Though FLIR has great potential for burn wound assessment, its use for this has not been well documented. Thus, we have conducted a systematic review and meta-analysis of the current use of FLIR technology to assess burn depth and healing potential. Methods A systematic review of the literature was performed on PubMed and Google Scholar between June 2020-August 2020 using the following keywords: thermal imaging, FLIR, forward looking infrared, burn, burn depth. Meta-analysis was performed on the mean sensitivity and specificity of the ability of FLIR to predict healing potential. Inclusion criteria were articles investigating the use of FLIR for burn wound assessments in adults, pediatric patients and animal models. Reviews and non-English articles were excluded. Results A total of 11 articles were included in the final review. Statistically significant correlations were found between FLIR and laser doppler imaging (LDI) in 3/3 clinical studies. A case report of a single patient found that FLIR was more accurate than LDI for assessing burn depth. Three articles investigated the ability of FLIR to predict healing potential, with all three reporting statistically significant results. Significant temperature differences between burnt and unburnt skin were found in 2/2 articles. FLIR was compared to clinical assessment by burn surgeons in two articles; one article found that FLIR was more accurate for assessing burn depth, while the other article found that clinical assessment was more accurate for predicting healing potential < 21 days. Mean sensitivity and specificity of the ability of FLIR to determine healing potential < 15 days was 44.5 and 98.8 respectively. Mean sensitivity and specificity of the ability of FLIR to determine healing potential < 21 days was 44.0 and 77.4 respectively. Conclusions FLIR is an accurate, simple, and cost-effective method of burn wound assessment. FLIR has been demonstrated to have significant correlations with other methods of assessing burns such as LDI and can be utilized to accurately assess burn depth and healing potential.

DeepTarget: An Automatic Target Recognition Using Deep Convolutional Neural Networks

Automatic target recognition (ATR) is an important part for many computer vision applications. Despite the extensive research which has been carried out in this area for many years, there is no ATR system which performs well on all applications. Recently, different object recognition frameworks have been proposed which yield a high performance in baseline databases. However, our experiments showed that they can fail in real-world scenarios, when dealing with a limited number of data samples. In this paper, we propose a new ATR system, based on deep convolutional neural network (DCNN), to detect the targets in forward looking infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, the corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases.

Infrared-Based Autonomous Navigation for Civil Aircraft Precision Approach and Landing

Precise navigation is a fundamental problem of aircraft safety approach and landing. However, the existing methods, including rotorcraft-based and fixed-wing-based, cannot meet the requirements of precision approach and landing of civil aircraft in the global position system (GPS)-denied and low visibility. This paper proposes an autonomous approach and landing navigation method whose accuracy is comparable to Inertial/Differential GPS (DGPS) integration. This method integrates inertial data, forward-looking infrared (FLIR) images, and runway geographic information to estimate kinetics states of aircraft during approach and landing. First, we improve an existing method to robustly detect runway, accurately extract three vertexes of runway contour from FLIR images, and synthesize the virtual runway features by runway geo-information and aircraft's pose parameters. Second, we propose to use real and synthetic runway features to create vision cues and integrate them with inertial data in square-root unscented Kalman filter to estimate the motion errors. Meanwhile, the measured motion states are corrected with the estimated state errors. Finally, we design a flight data acquisition platform equipped on a general aircraft and use the real flight data to verify our proposed method. The experimental results demonstrate that the proposed method can run smoothly for civil aircraft precision approach and landing.

Mobile forward-looking infrared technology allows rapid assessment of resuscitative endovascular balloon occlusion of the aorta in hemorrhage and blackout conditions.

Objective assessment of final resuscitative endovascular balloon occlusion of the aorta (REBOA) position and adequate distal aortic occlusion is critical in patients with hemorrhagic shock, especially as feasibility is being increasingly investigated in the prehospital setting. We propose that mobile forward-looking infrared (FLIR) thermal imaging is a fast, reliable, and noninvasive method to assess REBOA position and efficacy in scenarios applicable to battlefield and prehospital care. Ten swine were randomized to a 40% hemorrhage group (H, n = 5) or nonhemorrhage group (NH, n = 5). Three experiments were completed after Zone I placement of a REBOA catheter. Resuscitative endovascular balloon occlusion of the aorta was deployed for 30 minutes in all animals followed by randomized continued deployment versus sham in both light and blackout conditions. Forward-looking infrared images and hemodynamic data were obtained. Images were presented to 62 blinded observers for assessment of REBOA inflation status. There was no difference in hemodynamic or laboratory values at baseline. The H group was significantly more hypotensive (mean arterial pressure 44 vs. 60 mm Hg, p < 0.01), vasodilated (systemic vascular resistance 634 vs. 938dyn·s/cm, p = 0.02), and anemic (hematocrit 12 vs. 23.2%, p < 0.01). Hemorrhage group animals remained more hypotensive, anemic, and acidotic throughout all three experiments. There was a significant difference in the temperature change (ΔTemp) measured by FLIR between animals with REBOA inflated versus not inflated (5.7°C vs. 0.7°C, p < 0.01). The H and NH animals exhibited equal magnitudes of ΔTemp in both inflated and deflated states. Blinded observer analysis of FLIR images correctly identified adequate REBOA inflation and aortic occlusion 95.4% at 5 minutes and 98.8% at 10 minutes (positive predictive value at 5 minutes = 99% and positive predictive value at 10 minutes = 100%). Mobile thermal imaging is an easy, rapid, and reliable method for assessing distal perfusion after occlusion by REBOA. Smartphone-based FLIR technology allows for confirmation of adequate REBOA placement at the point of care, and performance was not degraded in the setting of major hemorrhage or blackout conditions.

FEASIBILITY STUDY OF INEXPENSIVE THERMAL SENSORS AND SMALL UAS DEPLOYMENT FOR LIVING HUMAN DETECTION IN RESCUE MISSIONS APPLICATION SCENARIOS

Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

FEASIBILITY STUDY OF INEXPENSIVE THERMAL SENSORS AND SMALL UAS DEPLOYMENT FOR LIVING HUMAN DETECTION IN RESCUE MISSIONS APPLICATION SCENARIOS

Abstract. Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

Forward-looking infrared imaging predicts ultimate burn depth in a porcine vertical injury progression model

IntroductionCurrent methods of assessing burn depth are limited and are primarily based on visual assessments by burn surgeons. This technique has been shown to have only 60% accuracy and a more accurate, simple, noninvasive method is needed to determine burn wound depth. Forward-looking infrared (FLIR) thermography is both noninvasive and user-friendly with the potential to rapidly assess burn depth. The purpose of this paper is to determine if early changes in burn temperature (first 3 days) can be a predictor of burn depth as assessed by vertical scarring 28 days after injury. MethodsWhile under general anesthesia, 20 burns were created on the backs of two female Yorkshire swine using a 2.5cm×2.5cm×7.5cm, 150g aluminum bar, for a total of 40 burns. FLIR imaging was performed at both early (1, 2 and 3 days) and late (7, 10, 14, 17, 21, 24 and 28 days) time points. Burns were imaged from a height of 12 inches from the skin surface. FLIR ExaminIR© software was used to examine the infrared thermographs. One hundred temperature points from burn edge to edge across the center of the burn were collected for each burn at all time points and were exported as a comma-separated values (CSV) file. The CSV file was processed and analyzed using a MATLAB program. The temperature profiles through the center of the burns generated parabola-like curves. The lowest temperature (temperature minimum) and a line midway between the temperature minimum and ambient skin temperature at the burn edges was defined and the area of the curve calculated (the “temperature half-area”). ResultsHalf-area values 2 days after burn had higher correlations with scar depth than did the minimum temperatures. However, burns that became warmer from 1 day to 2 days after injury had a lower scar depth then burns that became cooler and this trend was best predicted by temperature minima. When data were analyzed as a diagnostic test for sensitivity and specificity using >3mm scarring, i.e. a full-thickness burn, as a clinically relevant criterion standard, temperature minima at 2 days after burn was found to be the most sensitive and specific test. ConclusionsFLIR imaging is a fast and simple tool that has been shown to predict burn wound outcome in a porcine vertical injury progression model. Data showed that more severe burn wounds get cooler between 1 and 2 days after burn. We found four analytic methods of FLIR images that were predictive of burn progression at 1 and 2 days after burn; however, temperature minima 2 days after burn appeared to be the best predictive test for injury progression to a full-thickness burn. Although these results must be validated in clinical studies, FLIR imaging has the potential to aid clinicians in assessing burn severity and thereby assisting in burn wound management.

Moving Target Tracking in Infrared Image Sequences Based on Differential Kernel Covariance Descriptor

Forward looking infrared (FLIR) imaging has been used in many areas of research and everyday life, but it has been mostly employed in military and security domains. In these fields, remote infrared target tracking is a crucial element for surveillance. However, long-range captured IR image sequences generally have poor contrast, variable illumination, and high background clutter. These challenges make target tracking difficult. This paper suggests a technique for target tracking in different ranges in challenging FLIR image sequences, based on Differential Kernel Covariance Descriptor (DKCD). This new method diminishes rotation and illumination variation effects. The proposed technique calculates the differential kernel matrix of reference target by using various statistical and spatial features such as first and second derivatives, location information, and the intensity value of pixels. Later, the differential covariance matrix is constructed by using different pixel features and applying the appropriate kernel function to the matrix. Thanks to the kernel functions, the algorithm redefines the target's differential spatial features in Hilbert space. This process makes the descriptor non-linear. The predicted position of the target is calculated with the nearest neighbor algorithm in the candidate regions in the sub-frame. The performance of the suggested single target tracking system is then tested on challenging real-life video sequences.

Small object detection in forward-looking infrared images with sea clutter using context-driven Bayesian saliency model

There are two common challenges for small object detection in forward-looking infrared (FLIR) images with sea clutter, namely, detection ambiguity and scale variance. This paper presents a context-driven Bayesian saliency model to deal with these two issues. By inspecting the camera geometry of the FLIR imaging under the background of sea and sky, we observed that there exists dependency relationship between the locations and scales at which objects may occur, and the context which is defined to be the location of horizon line. Based on this observation, we propose to incorporate contextual information into the basic bottom-up saliency computation, and a unified Bayesian model is developed to achieve this goal. The proposed model is generic and can be potentially applied to other circumstances where context is available for facilitating object detection. Experimental results have demonstrated the effectiveness of our method.

Rozpoznawanie obiekt�w morskich na podstawie obraz�w FLIR metod� analizy obraz�w w�asnych (eigenimages)

This paper presents a method of recognition of maritime objects based on FLIR (forward looking infra-red) sensor images using eigenimages analysis method. The method is an extension of Principal Component Analysis (PCA) method. In last part of the paper are presented preliminary test results of the classification method for a set of FLIR images registered in the Baltic Sea. (Recognition of maritime objects based on FLIR images using eigenimages analysis method. A method of recognition of maritime objects based on FLIR (forward looking infra-red) sensor images Slowa kluczowe: rozpoznawanie obrazow FLIR, segmentacja, metoda analizy glownych skladowych PCA, metoda obrazow wlasnych.

Advances in target detection and tracking in Forward-Looking InfraRed (FLIR) imagery.

Here we give context to the Special Issue on “Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery” in Sensors. We start with an introduction to the role of infrared images in today's vision-based applications, by outlining the specific challenges that characterize detection and tracking in FLIR images. We then illustrate why selected papers have been chosen to represent the domain of interest, by summarizing their main contributions to the state-of-the-art. Lastly, we sum up the main evidence found, and we underline some of the aspects that are worthy of further investigation in future research activities.

Recognition of Airport Runways in FLIR Images Based on Knowledge

Airport runway recognition technology would play an important role in developing intelligent weapon systems in the future. In this letter, a method of automatically finding runways in forward looking infrared (FLIR) images is proposed based on the knowledge of vision. First, the line segments in the images are extracted by a fast line segment detector (LSD) and an improved line segment linking method. Then, the regions of interest (ROI) of runways are detected using some constraint rules based on the direction, gradient, and width of line segment pairs. Afterward, an ROI length backtracking technique based on texture distribution is presented to retrieve the complete ROI. Finally, using runway regional self-similarity and contextual information, several decision criteria are formulated to accurately recognize the runway. Experimental results on the FLIR images with different imaging ranges show that the proposed algorithm is robust and has a good real-time performance.

Trends in correlation-based pattern recognition and tracking in forward-looking infrared imagery.

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences.

Factors Influencing the Efficacy of Forward-Looking Infrared in Polar Bear Den Detection

Female polar bears construct maternal dens in snowdrifts in autumn. Forward-looking infrared (FLIR) has been used to locate dens to prevent disruption of denning by human activities, but the results have been mixed. To identify limitations and optimal conditions for locating dens, we took handheld FLIR images of three artificial dens under varied conditions. We tested variables hypothesized to influence detectability with linear models using the zero-inflated negative binomial distribution. Solar radiation, wind speed, and den wall thickness reduced the likelihood of detecting dens. The negative effect of wind speed on detectability increased with increasing distance. To maximize the efficacy of ground-based FLIR, den surveys should be conducted when solar radiation is less than 16 watts per square meter (night) and when wind speed is less than 10 kilometers per hour. Adherence to these guidelines will maximize the protection that FLIR can afford to denning bears.

Indirect target detection method in FLIR image sequences

Due to the complexity of the scene, target detection in forward-looking infrared (FLIR) imagery is a challenging problem, especially for occluded target. The main contribution of this paper is to propose an indirect detection method for improving the recognition probability and effectiveness of target detection method in FLIR image sequences under complex conditions. The proposed method mainly includes four steps: preparation of forward-looking reference image of landmark, extraction of the real-time scene image, template matching and target location, in which some key technologies are proposed, such as perspective transformation used to solve projective problems, position prediction for improving real-time performance, and target location used for identifying the target’s position. Experimental results are shown to demonstrate the robustness and efficiency of proposed method in FLIR image sequences.

Automatic target recognition and tracking in forward-looking infrared image sequences with a complex background

This paper presents a technique for automatic airborne target recognition and tracking in forward-looking infrared (FLIR) images with a complex background. An image splitting and merging method is applied for detecting target signals. The presence of a complex background due to clouds and sun glint generates clutter in the image with the resulting possibility of false alarms. A Bayesian classifier trained using the NMI (normalized moment of inertia) feature is proposed for efficient clutter rejection. After classification, target candidates are entered into a tracking filter. As an efficient and robust multi-target tracking filter in cluttered environments, the JDC-JIHPDAF is proposed. Experimental results using a wide range of real FLIR images ensure reliable classification and automatic target recognition performance.

A Coarse-to-Fine Matching Algorithm for FLIR and Optical Satellite Image Registration

The registration of a forward-looking infrared (FLIR) image and an optical satellite image (visible image) is challenging but important for image-based navigation systems. To solve this problem effectively, a coarse-to-fine matching algorithm is proposed. First, geometric rectification based on the attitude angles and height parameter is carried out to eliminate the distinct rotation and scale discrepancies between the FLIR and visible images. Then, in the fine registration step, the edges of the visible image and rectified infrared image are extracted, and a robust point set registration algorithm which can deal with the similarity transformation distortion is proposed. Finally, the experiments on both the simulated images and real images show that our algorithm can achieve excellent performance in terms of both robustness and accuracy, and the registration precision of real images can be around one pixel.

Target extraction in forward-looking infrared images using fuzzy thresholding via local region analysis

In this paper, a target extraction method in forward-looking infrared (FLIR) images based on fuzzy thresholding which used local characteristics, bi-modality and adjacency, is proposed. The bi-modality represents how a pixel is classified into a part of a target using distribution of pixel values in a local region, and the adjacency is a measure to represent how far each pixel is from the target region. Segmentation is processed by the following: First, membership values for each pixel are calculated using bi-modality and adjacency. Second, fuzzy thresholding is performed to extract the target from the background. Finally, we extract the precise target in the thresholded image by post-processing. To evaluate the performance of the proposed target extraction method, we compare the proposed method with other segmentation methods using various FLIR images. Experimental results show that the proposed algorithm has good segmentation performance.

Method for building recognition from FLIR images

Herein, a method for building recognition is presented in forward looking infrared (FLIR) images with clutter background, which is composed of the several sub-procedures. In the first phase, a three-dimensional (3-D) target model is generated and the model features are predicted based on the sensor's perspective relative to the 3-D target model. The second phase of the process, multi-scale structuring elements are generated pertaining to the 3-D target model and flight trajectory. Structuring elements for infrared image is selected by a look-up-table approach based on the parameters of sensor's view, and the use of morphology-based filters can respond to the size and shape of target to suppress the clutter background. In the following process, iterative segmentation for the result image of background suppression is used to obtain regions of interest (ROIs), and features extraction of ROIs and matching retain the ROIs that are closest to predicted features. Lastly, the target is identified by fusing the line features and multi-frame integration. Experiment results show the proposed algorithm can precisely recognize the target from FLIR images with a complicated background.

BUILDING RECOGNITION BASED ON INDIRECT LOCATION OF PLANAR LANDMARK IN FLIR IMAGE SEQUENCES

A novel method is proposed to deal with the problem of building recognition in forward-looking infrared (FLIR) image sequences. Two computational models are deduced in this paper: one is the perspective transformation model, through which an image is transformed perspectively from downward-looking state to forward-looking state; the other is the indirect location model, through which the position of a building is computed in an FLIR image. In addition, in order to illustrate the application scope of our method, the error analysis is presented. The proposed approach is validated by extensive experiments, with images taken in different weather conditions, seasons and at different times. Superior recognition results were obtained on three FLIR image sequences we collected.