Application of remote thermal imaging and night vision technology to improve endangered wildlife resource management with minimal animal distress and hazard to humans

AbstractBackgroundLocal envenomation following snakebites is accompanied by thermal changes, which could be visualized using infrared imaging. We explored whether infrared thermal imaging could be used to differentiate venomous snakebites from non-venomous and dry bites.MethodsWe prospectively enrolled adult patients with a history of snakebite in the past 24 hours presenting to the emergency of a teaching hospital in southern India. A standardized clinical evaluation for symptoms and signs of envenomation including 20-minute whole-blood clotting test and prothrombin time was performed to assess envenomation status. Infrared thermal imaging was done at enrolment, 6 hours, and 24 hours using a smartphone-based device under ambient conditions. Processed infrared thermal images were independently interpreted twice by a reference rater and once by three novice raters.FindingsWe studied 89 patients; 60 (67%) of them were male. Median (IQR) time from bite to enrolment was 11 (6.5—15) hours; 21 (24%) patients were enrolled within 6 hours of snakebite. In all, 48 patients had local envenomation with/without systemic envenomation, and 35 patients were classified as non-venomous/dry bites. Envenomation status was unclear in six patients. At enrolment, area of increased temperature around the bite site (Hot spot) was evident on infrared thermal imaging in 45 of the 48 patients with envenomation, while hot spot was evident in only 6 of the 35 patients without envenomation. Presence of hot spot on baseline infrared thermal images had a sensitivity of 93.7% (95% CI 82.8% to 98.7%) and a specificity of 82.9% (66.3% to 93.4%) to differentiate envenomed patients from those without envenomation. Interrater agreement for identifying hot spots was more than substantial (Kappa statistic >0.85), and intrarater agreement was almost perfect (Kappa = 0.93). Paradoxical thermal changes were observed in 14 patients.ConclusionsPoint-of-care infrared thermal imaging could be useful in the early identification of non-venomous and dry snakebites.Author summaryMost poisonous snakebites cause swelling of the bitten body part within a few hours if venom had been injected. Usually, health care providers diagnose poisonous snakebites by doing a clinical examination and by testing for incoagulable blood. If no abnormalities are found, then the snakebite is diagnosed as a non-poisonous bite or a dry bite. Swelling of the bitten body part results from venom-induced inflammation and is accompanied by local increase in skin temperature. It is possible to capture visual images of these temperature changes by using infrared imaging, the same technology used in night vision cameras. This study found that most persons with poisonous snakebites had hot areas on infrared images while such changes were observed in only a few persons with non-poisonous or dry snakebites. This new knowledge could help doctors identify non-poisonous and dry snakebites early.

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A Semi-Autonomos Drone for Surveillance and Security

INCAS BULLETIN ◽

10.13111/2066-8201.2020.12.4.25 ◽

2020 ◽

Vol 12 (4) ◽

pp. 267-270

Author(s):

VISHAL JANGRA ◽

SUNIL

Keyword(s):

Real Time ◽

Thermal Imaging ◽

Control Unit ◽

Night Vision ◽

Rescue Operation ◽

Metal Detection ◽

Autonomous Aircraft

A semi-autonomous aircraft to provide a real time surveillance in combat and rescue operation. It will fly autonomously ahead of user to find out the threats like distant enemies and traps like landmines. It will provide data about their position and things or ammunitions they have. It will use infrared and thermal imaging for night vision and powerful metal detection to find landmines and other traps. All data can be transmitted directly to the user on a helmet display or wrist screen. It will also provide warning about its findings. For positioning the threats it will provide GPS coordinates of threat. User can also operates it using the control unit.

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Estimation of Effect of Emissivity on Target Detection through Thermal Imaging Systems

Defence Science Journal ◽

10.14429/dsj.67.9821 ◽

2017 ◽

Vol 67 (2) ◽

pp. 177 ◽

Cited By ~ 2

Author(s):

Amit Kumar Sharma ◽

Sanjay Kumar Sharma ◽

Prashant Vasistha ◽

Jagdish Prasad Mangalhara

Keyword(s):

Thermal Imaging ◽

Background Radiation ◽

Thermodynamic Temperature ◽

Night Vision ◽

Thermal Imager ◽

Thermal Contrast ◽

Detection Range ◽

Thermal Imagers ◽

Long Wave Infrared ◽

Ir Bands

<p>The effects of target emissivity on apparent thermal contrast as well as on detection range capabilities of thermal imagers in long wave infrared and middle wave infrared bands were evaluated. The apparent thermal contrast (to be seen by the thermal imager at standoff distance), considering only the emission from target and background, was first computed in both the IR bands in terms of target emissivity and secondly the apparent thermal contrast, considering the background radiation reflected off the target, was also computed. A graphical user interface simulation in MATLAB was prepared for the estimation of total apparent thermal contrast taking into account both the emission and reflection. This total apparent thermal contrast was finally used in night vision thermal and image processing model for predicting the detection range performance of thermal imagers. Results of the analysis show that the effect of target emissivity on thermal contrast estimates is more pronounced in LWIR. The lower thermodynamic temperature difference between target and background at lower values of target emissivity leads to negative thermal contrast which in-turn leads to higher detection ranges.</p>

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Матричные фотоприемные устройства ИК‑диапазона: «постпандемические» тенденции развития. Часть II

Photonics Russia ◽

10.22184/1993-7296.fros.2020.14.4.320.330 ◽

2020 ◽

Vol 14 (4) ◽

pp. 320-330

Author(s):

Н.А. Кульчицкий ◽

А.В. Наумов ◽

В.В. Старцев

Keyword(s):

Spectral Range ◽

Thermal Imaging ◽

Infrared Detectors ◽

Growth Dynamics ◽

Night Vision ◽

Security Systems ◽

Imaging Technology ◽

Market Growth ◽

Thermal Detectors

В первой части обзора ( см. Фотоника. 2020; 14(3): 234–244) были рассмотрены инфракрасные детекторы тепловизионной техники. Устройства востребованы в системах и комплексах гражданской и медицинской термографии, охранного и пожарного наблюдения, персональных системах ночного видения и обеспечения безопасности. Во второй части обзора рассмотрены охлаждаемые МФПУ для спектрального диапазона 3–5 мкм, 8–12 мкм, неохлаждаемые МФПУ. Представлено сравнение тепловых детекторов разного типа от разных мировых производителей. Дан экспертный прогноз изменений динамики роста рынка и его пост-пандемические тенденции развития. The first part of the review (see Photonics Russia.2020; 14(3): 234–244) deals with infrared detectors of thermal imaging technology. The devices are in demand in systems and complexes of civil and medical thermography, security and fire surveillance, personal night vision and security systems. The second part of the review deals with cooled APDs for the spectral range of 3–5 microns, 8–12 microns, uncooled APDs. A comparison of thermal detectors of various types by different world manufacturers is presented. An expert forecast of changes in market growth dynamics and trends of its post-pandemic development is given.

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A Comparison of Soldier Performance on a Target Detection and Identification Task Using Fused Sensor Technology and Current Night Vision Technology

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120705101912 ◽

2007 ◽

Vol 51 (19) ◽

pp. 1332-1335

Author(s):

M. LaFiandra ◽

W. Harper

Keyword(s):

United States Army ◽

Thermal Imaging ◽

Night Vision ◽

Sensor Technology ◽

Identification Task ◽

Special Forces ◽

Detection And Identification ◽

Study Participants ◽

Main Effects ◽

Field Setting

Soldiers rely on night vision devices to enhance their ability to detect and identify objects of interest in environments of reduced luminosity. The night vision device that is currently being used by United States Army Soldiers deployed in Iraq and Afghanistan is based on Image Intensifying (I2) technology. An alternative technology for night vision devices is to use a fused sensor that combines I2 technology and a thermal sensor. The purpose of this study is to compare Soldier performance on detecting and identifying human targets while using a night vision device with I2 technology to their performance while using prototype fused sensors in a field setting. Five retired Special Forces Soldiers, all of which had experience with using night vision devices which employed I2 technology and with thermal imaging technology participated in the study. Participants were asked to detect targets at ranges varying from 50–250 meters and to identify targets as military or civilian at ranges of 25–150 meters. Significant main effects of type of night vision device type (p < 0.0093) and range (p < 0.0003) were found on the ability of the participants to both detect and identify targets. In addition, a significant interaction was determined (p < 0.0219) on the ability of participants to detect targets. Focused analysis revealed participants were more able to correctly detect targets with the I2 technology than with fused sensors at targets ranging from 200–250 meters, and were more able to correctly identify targets with I2 than with fused sensors. The results from this study clearly indicate the prototype fused sensor tested here did not outperform a night vision device based solely on I2 technology, and in several cases demonstrated poorer performance.

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Cadillac DeVille Thermal Imaging Night Vision System

10.4271/2000-01-0323 ◽

2000 ◽

Cited By ~ 9

Author(s):

Nancy S. Martinelli ◽

Scott A. Boulanger

Keyword(s):

Thermal Imaging ◽

Vision System ◽

Night Vision

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Infrared Devices And Techniques (Revision)

Metrology and Measurement Systems ◽

10.2478/mms-2014-0057 ◽

2014 ◽

Vol 21 (4) ◽

pp. 565-618 ◽

Cited By ~ 39

Author(s):

A. Rogalski ◽

K. Chrzanowski

Keyword(s):

Gas Sensors ◽

Thermal Imaging ◽

Thermal Emission ◽

Image Intensifier ◽

Night Vision ◽

Imaging Systems ◽

Infrared Techniques

Abstract The main objective of this paper is to produce an applications-oriented review covering infrared techniques and devices. At the beginning infrared systems fundamentals are presented with emphasis on thermal emission, scene radiation and contrast, cooling techniques, and optics. Special attention is focused on night vision and thermal imaging concepts. Next section concentrates shortly on selected infrared systems and is arranged in order to increase complexity; from image intensifier systems, thermal imaging systems, to space-based systems. In this section are also described active and passive smart weapon seekers. Finally, other important infrared techniques and devices are shortly described, among them being: non-contact thermometers, radiometers, LIDAR, and infrared gas sensors.

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Deep Learning Based Object and Railway Track Recognition Using Train Mounted Thermal Imaging System

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9342 ◽

2020 ◽

Vol 17 (11) ◽

pp. 5062-5071

Author(s):

Rajiv Kapoor ◽

Rohini Goel ◽

Avinash Sharma

Keyword(s):

Deep Learning ◽

Thermal Imaging ◽

Imaging System ◽

Night Vision ◽

Railway Track ◽

Validation Data ◽

Ir Camera ◽

Railway Tracks ◽

Railway Safety ◽

Thermal Imaging System

An intelligent railways safety system is very essential to avoid the accidents. The motivation behind the problem is the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. Continuous research is being carried out by distinct researchers to ensure railway safety and to reduce accident rates. In this paper, a novel method is proposed for identifying objects (obstacles) on the railway tracks in front of a moving train using a thermal camera. This approach presents a novel way of detecting the railway tracks as well as a deep network based method to recognize obstacles on the track. A pre-trained network is used that provides the model understanding of real world objects and enables deep learning classifiers for obstacle identification. The validation data is acquired by thermal imaging using night vision IR camera. In this work, the Faster R-CNN is used that efficiently recognize obstacles on the railway tracks. This process can be a great help for railways to reduce accidents and financial burdens. The result shows that the proposed method has good accuracy of approximately 83% which helps to enhance the railway safety.

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Deep Learning Based Object Recognition in Real Time Images Using Thermal Imaging System

10.3233/apc210215 ◽

2021 ◽

Author(s):

Rohini Goel ◽

Avinash Sharma ◽

Rajiv Kapoor

Keyword(s):

Object Recognition ◽

Real Time ◽

Thermal Imaging ◽

Imaging System ◽

Night Vision ◽

Driver Assistance ◽

Thermal Images ◽

Deep Network ◽

The One ◽

Thermal Imaging System

An efficient driver assistance system is essential to avoid mishaps. The collision between the vehicles and objects before vehicle is the one of the principle reason of mishaps that outcomes in terms of diminished safety and higher monetary loss. Researchers are interminably attempting to upgrade the safety means for diminishing the mishap rates. This paper proposes an accurate and proficient technique for identifying objects in front of vehicles utilizing thermal imaging framework. For this purpose, image dataset is obtained with the help of a night vision IR camera. This strategy presents deep network based procedure for recognition of objects in thermal images. The deep network gives the model understanding of real world objects and empowers the object recognition. The real time thermal image database is utilized for the training and validation of deep network. In this work, Faster R-CNN is used to adequately identify objects in real time thermal images. This work can be an incredible help for driver assistance framework. The outcomes exhibits that the proposed work assists to boost public safety with good accuracy.

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