Thermal Imaging Sensors Research Articles

Defect Detection Using Thermography Camera Techniques: A review

Individuals across different industries, including but not limited to agriculture, drones, pharmaceuticals and manufacturing, are increasingly using thermal cameras to achieve various safety and security goals. This widespread adoption is made possible by advancements in thermal imaging sensor technology. The current literature provides an in-depth exploration of thermography camera applications for detecting faults in sectors such as fire protection, manufacturing, aerospace, automotive, non-destructive testing and structural material industries. The current discussion builds on previous studies, emphasising the effectiveness of thermography cameras in distinguishing undetectable defects by the human eye. Various methods for defect detection, including temperature analysis and image processing algorithms, are thoroughly presented. The factors contributing to the effectiveness of thermography cameras are explored, along with their advantages over traditional inspection methods. The literature review highlights the diverse applications of thermography cameras in fault detection. The review highlights the remarkable transformation brought by thermal camera technology in mechanical system fault detection, leading to improved maintenance practices. These cameras can detect unseen irregularities, enable non-invasive testing and support hands-on system maintenance, making them indispensable tools for ensuring mechanical systems operate efficiently, reliably and safely. With the continuous advancement of technology, the integration of Industry 4.0 and IoT technologies will further enhance the capabilities of thermal cameras, ensuring elevated performance across different domains. In electrical systems, thermal cameras allow for the early identification of faults, enabling proactive maintenance to mitigate risks. Additionally, by assessing structural integrity, thermal cameras can detect thermal and insulation inefficiencies, leading to improved energy efficiency.

Multifunctional Thermal Imaging Complex for Medical Diagnostics

Introduction. Recent developments in the field of thermal imaging technology and the accumulated experience in instrumental analysis of thermal processes in the patient’s body indicate the prospects of developing new medical thermal imaging systems (thermal imagers).Aim. Creation of a low-budget domestic thermal imaging system for medical diagnostics with expanded functionality.Materials and methods. The distribution of temperature over the surface of the human body depends on its internal state and external environment. For each person, this distribution has its own physiological characteristics, the study and interpretation of which can be significant for diagnosing specific pathologies or assessing the general (psychophysical) state of the patient. Analysis of temperature fields on the surface of the human body makes it possible to diagnose various pathological processes manifested in the form of local temperature changes in individual areas. The proposed method for thermal diagnostics involves measuring the temperature at each point of such an area simultaneously (statically) or over a period of time (dynamically). In comparison with conventional approaches, two thermal imaging sensors are used for this purpose. These include a small-sized matrix (thus being low cost) and a point sensor. Such a combination of sensors provides for the necessary discretization of the temperature field image of sufficiently large areas of the surface of the human body, when shooting from a distance significantly reduced compared to the conventional approach.Results. A conceptual electrical circuit and a layout of a modern thermal imaging system are developed. The advantages of the proposed thermal imager over similar devices are assessed. A method for recording and analyzing temperature in a certain point and temperature fields of both separate areas and large areas on the surface of the human body is proposed and tested.Conclusion. The conducted testing of a material model of the proposed thermal imaging system using the facilities of large medical institutions in St. Petersburg showed its wide functionality along with simplicity and convenience.

A multispectral camera in the VIS–NIR equipped with thermal imaging and environmental sensors for non invasive analysis in precision agriculture

Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition. In this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS–NIR) and thermal (LWIR) range. Multispectral imaging is performed with a rotating wheel of interchangeable band-pass filters. The system is also equipped with a set of environmental sensors to acquire CO2 concentration values, light intensity, temperature, and relative humidity of the surrounding environment. The technology and the measurement protocol were experimentally validated in laboratory and in open field. Advantages with respect to the available MSI cameras mounted on UAV is the integrated imaging in both the reflectance and the thermal emissive band in a close-up imaging setup and the use of environmental sensors. From the multispectral stack the spectral signature of the plants can be obtained and various vegetation indices (e.g., NDVI, NDRE) can be calculated for investigating the health status of the plant, while thermography provide additional monitoring. Close-up multispectral imaging is expected to tackle the new challenges of precision agriculture by enabling the acquisition of high-quality dataset on single plants.

Thermal Cameras and Their Safety Implications for Pedestrian Protection: A Mixed Empirical and Simulation-Based Characterization

Multiple sensing technologies exist for advanced driver support and driving automation systems. Car manufacturers combine different solutions to increase the robustness and operational capabilities of driving automation technology perception systems. One sensing method that did not achieve high market penetration with respect to other solutions is the thermal camera. Thermal cameras can operate in low-light and high-contrast conditions as they do not need external sources of illumination. Instead, they are sensitive to the infrared radiation emitted by any surface above 0 K. Thus, thermal cameras can be a suitable candidate to compensate for the weaknesses of visible cameras. To establish the potential benefit of thermal cameras for pedestrian protection, this paper reports on an experiment using state-of-the-art thermal imaging sensors. The authors’ scientific effort includes a characterization of the infrared sensor capability to identify pedestrians when coupled with a detection algorithm and provides a simulation-based investigation of the potential safety benefits of thermal camera adoption. The analysis additionally includes a heuristic characterization of the detection capabilities of a passenger car equipped with visible cameras to provide a baseline comparison. Ultimately, the thermal sensors return noteworthy detection capability that could lead to a potentially significant improvement in road safety on rural roads provided that the sensor has sufficient resolution.

Thermal Image Based Occupant Count Measurement Model using Human Body Temperature for Smart Building

Objectives: The proposed Occupant Count Measurement (OCM) model aims to enhance sustainability, energy efficiency, comfort, and safety in smart buildings by accurately determining occupant count using thermal camera images and body temperature data. Methods: The model leverages real-time thermal camera images without the need for a pre-existing dataset. Key parameters include temperature threshold, occupant motion, size, and shape to ensure accurate occupancy estimation. The K-means algorithm identifies and clusters regions of interest (ROI) in thermal images corresponding to human body temperatures. The model also employs sensors like PIR, RGB cameras, and thermal image sensors. Manual counting serves as a benchmark for comparison. Findings: The K-means algorithm extracts regions with elevated temperatures related to human bodies from thermal images, partitioning them into K-clusters based on temperature ranges and assigning each pixel to one of the clusters. A temperature threshold differentiates human clusters in the thermal image, while connected component labeling refines human object segmentation by identifying blobs, which are then used for occupant counting. The model's precision is assessed using diverse image sensors and compared to the actual number of occupants. The proposed OCM model achieves an accuracy of about 90.2% compared to traditional methods. Novelty: This study introduces an OCM model that uses thermal images to estimate the number of occupants in a room based on their body temperature. The method focuses on detecting and counting occupants by their overall thermal body signature, providing a novel approach to occupant measurement in smart buildings. Keywords: Thermal Images, Segmentation, Occupant Estimation, Occupant Comfort, Smart Building

Occupant-centric cabin thermal sensation assessment system based on low-cost thermal imaging

Assessing cabin occupants' thermal sensation in real-time enables automatic control of car air conditioning, improving driving safety and energy efficiency. Traditional methods, limited by their need for numerous physiological parameters, have restricted practicality. For this purpose, this study employs a low-cost thermal imaging sensor as the hardware core to establish a cost-effective and non-contact real-time assessment system for the thermal comfort of occupants within a cabin. The system comprises a thermal sensation assessment model developed based on subject experiments, along with facial recognition and segmentation algorithms optimized for thermal images. The thermal sensation assessment model developed, which employs cheek temperature, solar radiation intensity, and cabin air temperature as its features, exhibited an R2 of 0.617 on the test set. Furthermore, the facial recognition algorithm established for thermographic imaging achieved a mean accuracy of 96.5% and a recall rate of 99.0%. The system underwent validation in real-world vehicle environments, proving its ability to accurately detect and measure the cheek temperatures of occupants in the cabin and execute thermal sensation assessments. With a mean absolute error of 0.5 thermal sensation units in its output, its accuracy in practical applications was affirmed. This research provides an effective solution for automatically adjusting cabin air conditioning.

Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

Pre-prediction and early detection are important for the prevention of fires. Hence, the integration of intelligent CCTV image analysis technology, deep learning, and artificial intelligence is actively being studied. Intelligent CCTV can analyze visible light images of fires, but the accuracies of the detection targets are low. To solve this problem, a dual sensing experimental device was constructed by combining a visible light sensor and a thermal image sensor, and an algorithm was implemented to predict fire events. An algorithm was constructed to detect three classes (Fire, Smoke, and Person) through RGB sensors and thermal imaging sensors. Additionally, a CNN deep learning method and the YOLOv5 method were used to build a classification neural network. The dual sensing device was able to act as a trigger to predict and detect a fire based on the size of the flame and changes in temperature.

Fine Pitch Micro Indium Bump Interconnect Flip Chip Bonding

Quantum computing processors, micro LED displays and Focal Plane Array (FPA) imaging and detector devices such as infrared (IR) thermal imaging sensors are seeing higher demand as more practical applications requiring these components are coming into research and development, industrial, military and consumer markets. This paired with higher pixel and Qubit count and interconnect density, on larger and larger chips is driving hybridization and monolithic integration in these technologies. This is showing a marked increase in demand for fine pitch micro bump interconnection flip chip die bonding. However, some critical challenges facing these technologies are; larger component sizes mean higher density interconnections over increasing surface area. Submicron accuracy required to align fine pitch micro interconnect arrays. This together with the challenges facing the materials that are becoming the industry standard for these applications, such as the requirement for the assembled components to remain stable in extreme conditions such as cryogenic application environments. Combined with low loss high strength mechanical/ electrical interconnect requirements on components containing sensitive materials, structures and unmatched coefficient of thermal expansion (CTE) means that processing gases such as formic acid or high temperature reflow bonding can no longer be used to bond these devices. These challenges mean that the industry is fast approaching the limitations of even state-of-the-art die bonders and die bonding methods on the market today. This paper is going to highlight these challenges and the methods used to address them to produce large format, high density IR thermal imaging FPA devices, Quantum processors and micro LED displays using fine pitch micro Indium bump array interconnections that meet today’s industry requirements.

Artificial intelligence technologies utilization for detecting explosive materials

Explosive material detection considers the identification and classification of explosive materials using techniques from traditional sniffer dogs to cutting-edge sensing technology like thermal imaging, X-ray scanners, and chemical sensors. Explosive detection is applied in various locations, including airports, government buildings, and public areas, to prevent terrorist attacks and criminal actions that attempt to employ explosive devices. The effectiveness of these procedures is dependent on the detection materials, equipment, and environment, so new techniques are continuously explored to increase precision, sensitivity, and detection speed. Because explosive substances present a critical risk to infrastructure, security, and public safety, extensive analysis of existing detection methods is needed. This paper highlights key areas for further research and development in explosive materials detection by addressing identified limitations and challenges. Specifically, advancements in technology, interdisciplinary collaboration, and the integration of AI techniques offer significant opportunities for improving detection accuracy, reducing false positives, and ensuring safer environments for individuals and society.

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

Luminescence-Based Infrared Thermal Sensors: Comprehensive Insights.

Recent chronological breakthroughs in materials innovation, their fabrication, and structural designs for disparate applications have paved transformational ways to subversively digitalize infrared (IR) thermal imaging sensors from traditional to smart. The noninvasive IR thermal imaging sensors are at the cutting edge of developments, exploiting the abilities of nanomaterials to acquire arbitrary, targeted, and tunable responses suitable for integration with host materials and devices, intimately disintegrate variegated signals from the target onto depiction without any discomfort, eliminating motional artifacts and collects precise physiological and physiochemical information in natural contexts. Highlighting several typical examples from recent literature, this review article summarizes an accessible, critical, and authoritative summary of an emerging class of advancement in the modalities of nano and micro-scale materials and devices, their fabrication designs and applications in infrared thermal sensors. Introduction is begun covering the importance of IR sensors, followed by a survey on sensing capabilities of various nano and micro structural materials, their design architects, and then culminating an overview of their diverse application swaths. The review concludes with a stimulating frontier debate on the opportunities, difficulties, and future approaches in the vibrant sector of infrared thermal imaging sensors.

Design and Implementation of Raspberry Pi Face Recognition + Thermal Imaging Temperature Measurement and Body Temperature Data Cloud Processing System

For temperature measurement in public places, this paper proposes and realizes a Raspberry Pi face recognition + thermal imaging temperature measurement and temperature data cloud processing system. This system uses a Raspberry Pi device as the temperature measurement terminal, connects the MLX90640 thermal imaging sensor and the BME280 environmental sensor for data collection, and calculates the core temperature of the human body from the collected data. The temperature measurement terminal uses Python language with OpenCV library for image processing, Alibaba Cloud API for face recognition, and the server side uses Python language with Django library for development. The system can quickly measure temperature and count the temperature measurement list, and view it in the cloud in real time to improve management efficiency.

Recent advances in thermal imaging and its application in military

Infrared cameras are versatile technology studied and utilized in enormous distinct applications ranging from human life-supporting fields such as industrial, building science, and medical to research and development area. Especially the exploitation of thermal imaging of infrared detectors is a potential candidate in military equipment and weapon for observing the target accurately in long-distance with many obstacles. The modification in thermal imaging sensors structure with uncooled and cooled detectors provides new concepts of incredibly sensitive devices with thermal. Here, the technical developments in Gen-generation devices for thermal imaging are discussed and studied outstandingly.

Combining thermal imaging and soil water content sensors to assess tree water status in pear trees.

Volumetric soil water content is commonly used for irrigation management in fruit trees. By integrating direct information on tree water status into measurements of soil water content, we can improve detection of water stress and irrigation scheduling. Thermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees (Pyrus communis L.). These indicators are easy, quick, and cost-effective. The soil and tree water status of two cultivars of pear trees 'D'Anjou' and 'Bartlett' submitted to regulated deficit irrigation was measured regularly in a pear orchard in Rock Island, WA (USA) for two seasons, 2021 and 2022. These assessments were compared to the canopy temperature (Tc), the difference between the canopy and air temperature (Tc-Ta) and the crop water stress index (CWSI). Trees under deficit irrigation had lower midday stem water potential and stomatal conductance but higher Tc, Tc-Ta, and CWSI. Tc was not a robust method to assess tree water status since it was strongly related to air temperature (R = 0.99). However, Tc-Ta and CWSI were greater than 0°C or 0.5, respectively, and were less dependent on the environmental conditions when trees were under water deficits (midday stem water potential values< -1.2 MPa). Moreover, values of Tc-Ta = 2°C and CWSI = 0.8 occurred when midday stem water potential was close to -1.5 MPa and stomatal conductance was lower than 200 mmol m-2s-1. Soil water content (SWC) was the first indicator in detecting the deficit irrigation applied, however, it was not as strongly related to the tree water status as the thermal-based indicators. Thus, the relation between the indicators studied with the stem water potential followed the order: CWSI > Tc-Ta > SWC = Tc. A multiple regression analysis is proposed that combines both soil water content and thermal-based indices to overcome limitations of individual use of each indicator.

Where should the thermal image sensor of a smart A/C look?-Occupant thermal sensation model based on thermal imaging data

Applying thermal imaging sensor to air conditioner for monitoring human thermal sensation and achieving dynamic settings may satisfy occupants' thermal needs while saving energy. The existing studies are mostly based on single-view imaging to build the model and ignore the possible differences in body surface temperature on thermal sensation response by gender, etc., which may have many limitations. Subject experiments were conducted in an artificial climate chamber to obtain subjective questionnaires and thermal images of the exposed frontal face, lateral face, top of the head, forearm, and hand dorsum of 27 subjects in this study. By applying machine learning classification algorithms and global optimal regression algorithms, the temperature collection zones that can accurately reflect the thermal sensation of both genders in each view were analyzed, and a two-stage thermal sensation assessment model applicable to multiple views was developed. Of the various imaging views, the frontal view of the face is the best, followed by the lateral view of the face, the top view of the head, and the forearm/hand dorsum view. For male and female, the mean absolute errors of the thermal sensation assessment model established were 0.41–0.49 and 0.50–0.53 thermal sensation units. In addition, gender differences were found in the response of head surface temperatures to thermal sensation. The results obtained can provide a reference for the application of thermal image sensor to smart air conditioners.

Enhanced Performance of Ru-Based Infrared Imaging Sensor Array With Electrospun Thermal Isolation Structure

Predicting and optimizing thermal conductivity of materials used in infrared thermal imaging sensors is an effective method to improve thermal isolation performance, which can substantially reduce the thermal crosstalk among pixels. This paper reports an electrospun P(VDF-TrFE) nanofiber film used as adiabatic support structure in thermal sensor array, and investigates its effect on imaging performance of thermal sensor. Based on solid heat transfer simulation, the thermal conductivities of electrospun P(VDF-TrFE) films with different packing ratios were theoretically calculated, showing a significant decrease compared with the spin-coated counterpart. The temperature increment of thermal sensor using nanofiber film with 43.71% packing ratio is calculated as 1.68 times higher than that using spin-coated film. An average temperature rise of 1.73 times was experimentally obtained, and the thermal response time was also reduced to half, compared with the sensor using spin-coated film. The method of improving thermal isolation performance using electrospun fibrous film could be of great advantage to high sensitivity infrared imaging sensor.

Recent Advances in Thermal Imaging and its Applications Using Machine Learning: A Review

Recent advancements in thermal imaging sensor technology have resulted in the use of thermal cameras in a variety of applications, including automotive, industrial, medical, defense and space, agriculture, and other related fields. Thermal imaging, unlike RGB imaging, does not rely on background light, and the technique is nonintrusive while also protecting privacy. This review article focuses on the most recent advancements in thermal imaging technology, key performance parameters, an overview of its applications, and machine-learning techniques applied to thermal images for various tasks. This article begins with the most recent advancements in thermal imaging, followed by a classification of thermal cameras and their key specifications, and finally a review of machine-learning techniques used on thermal images for various applications. This detailed review article is highly useful for designing thermal imaging-based applications using various machine-learning techniques.

Monocular thermal imaging for pedestrian detection and ranging

To react to the presence of pedestrians, an automated braking system must first find the pedestrian(s) including locations relative to the automobile both in angular position and in distance. In the daytime, cameras and radar can provide the necessary information, but this combination, which requires ambient or active illumination, fails at night. Passive thermal sensors are now being enlisted to dramatically improve imaging at night, whereas substantial effort is underway to assure proper fusing of object information from the thermal sensor with other sensors on the automobile. To simplify the acquisition of information needed to make valid automated braking decisions at night, a camera system with a thermal image sensor was developed that identifies pedestrians and labels each identified pedestrian with location and distance data. The camera utilizes a single uncooled custom microbolometer sensor and a software suite implementing artificial intelligence and machine learning capabilities, running on a combination of convolutional neural networks and fusion processing to provide the data that an automobile host computer needs to implement fast, safe, and accurate automatic braking. We present details of the system construction and operation as well as initial test results showing the potential this technology has to dramatically reduce pedestrian fatalities at night as well as augment safety across all conditions, whether day, night, fog, rain, snow, dust, sun glare, or headlight glare.

A longitudinal study of the occupancy patterns of a university library building using thermal imaging analysis

ABSTRACT Current debates around the ‘performance gap’ have highlighted the need to study building occupancy patterns to improve design solutions and better understand space utilisation. However, capturing occupancy data is resource intensive. There is a need for solutions that gather real-time occupancy data while maintaining the users’ privacy. In response to this challenge, this paper discusses applying a thermal imaging-based method for measuring occupancy in buildings and generating behavioural insights. A longitudinal analysis of the occupancy patterns over a full academic year is conducted for a university library building in the UK. The granular data collected through the thermal imaging analysis reveal insights into the building’s occupancy patterns over academic terms and vacation periods. The findings debunk conventional conceptions of library use during weekends/weekdays and terms/vacations. The application of thermal imaging sensors to monitor occupancy within the library building suggests the potential use of real-time data to improve the library’s space and organisational management. The paper makes a case for having an occupancy monitoring strategy in place that corresponds to the data needed for making effective interventions.

Health Status of Oilseed Rape Plants Grown under Potential Future Climatic Conditions Assessed by Invasive and Non-Invasive Techniques

Environmental conditions affect many plant traits such as biochemistry, physiology, morphology, and even their distribution around the world. Human activities have increased greenhouse gas emissions, which will promote a global rise in temperatures. The impact of climate change on natural vegetation and crops is difficult to predict, making it necessary to conduct experiments that mimic potential future climate conditions. Here, oilseed rape has been grown under environmental conditions that reproduce severe and intermediate climate change, setting the current climatic conditions as a control, with the main objective of evaluating the impact of climate change on the health status of this plant of agronomic interest. For such a purpose, two approaches (invasive and non-invasive) have been applied. Invasive quantitative measurements are based on the absorbance of biochemical compounds. Non-invasive methods such as thermal, multicolor fluorescence, and hyperspectral reflectance imaging sensors rely on the spectral properties of the plants. The results revealed that climate change induced lipid peroxidation, as well as alterations in pigment composition, transpiration, photosynthesis, and secondary plant metabolism. Those changes were more drastic the more severe the climatic condition imposed. Novel vegetation indices obtained from hyperspectral reflectance and specifically tailored to detect stress in brassicas correlated with physiological traits such as lipid peroxidation and secondary plant metabolism.