First part of this thesis describes possibilities of energy assessment of buildings, possibilities of using thermal imaging camera in civil engineering and using software solutions of energy assessment of buildings. Second part engages an energy assessment of administrative building of NEPA company in Brno and designes possibilities of improvements. Third part contains description of technical solution of the best improvement possibility.

Neonatal necrotizing enterocolitis (NEC) is one of the most severe digestive tract emergencies in neonates, involving bowel edema, hemorrhage, and necrosis, and can lead to serious complications including death. Since it is difficult to diagnose early, the morbidity and mortality rates are high due to severe complications in later stages of NEC and thus early detection is key to the treatment of NEC. In this thesis, a novel automatic image acquisition and analysis system combining a color and depth (RGB-D) sensor with an infrared (IR) camera is proposed for NEC diagnosis. A design for sensors configuration and a data acquisition process are introduced. A calibration method between the three cameras is described which aims to ensure frames synchronization and observation consistency among the color, depth, and IR images. Subsequently, complete segmentation procedures based on the original color, depth, and IR information are proposed to automatically separate the human body from the background, remove other interfering items, identify feature points on the human body joints, distinguish the human torso and limbs, and extract the abdominal region of interest. Finally, first-order statistical analysis is performed on thermal data collected over the entire extracted abdominal region to compare differences in thermal data distribution between different patient groups. Experimental validation in a real clinical environment is reported and shows encouraging results.

Infrared (IR) imaging devices based on energy detection has shown a dramatic development in technology along with an impressive price reduction in recent years. However, for a low-end market as in automotive applications, the present cost of IR cameras is still the main obstacle to broadening their usage. Ongoing research has continuously reduced the system cost. Apart from decreasing the cost of infrared optics, there are other key issues to achieve acceptable system costs, including wafer-level vacuum packaging of the detectors, low vacuum level operation, and the use of standard materials in the detector fabrication. This thesis presents concepts for cost reduction of low-end IR cameras.      The thesis presents a study of detector performance based on the thermal conductance design of the pixel. A circuit analog is introduced to analyze the basic thermal network effect from the surrounding environment on the conductance from the pixel to the environment. A 3D simulation model of the detector array conductance has been created in order to optimize the performance of the arrays while operated in low vacuum. In the model, Fourier's law of heat transfer is applied to determine the thermal conductance of a composite material pixel. The resulting thermal conductance is then used to predict the performance of the detector array in low vacuum.      The investigations of resist as the intermediate bonding material for 3D array integration are also reported in the thesis. A study has been made of the nano-imprint resists series mr-I 9000 using a standard adhesive wafer bonding scheme for thermosetting adhesives. Experiments have been performed to optimize the thickness control and uniformity of the nano-imprint resist layer. The evaluation, including assessment of the bonding surface uniformity and planarizing ability of topographical surfaces, is used to demonstrate the suitability of this resist as sacrificial material for heterogeneous detector array integration.      Moreover, the thesis presents research in wafer-level packaging performed by room temperature bonding. Sealing rings, used to create a cavity, are manufactured by electroplating. The cavity sealing is tested by liquid injection and by monitoring the deflection of the lid membrane of the cavities. A value for the membrane deflection is calculated to estimate the pressure inside the cavities.

Different type of images provided by various combinations of cameras have the power to help increase and optimize plant growth. Along with a powerful deep learning model, for the purpose of detecting these stress indicators in RGB images, can significantly increase the harvest yield. The field of AI solutions in agriculture is not vastly explored and this thesis aims to take a first step in helping explore different techniques to detect early plant stress. Within this work, different types and combinations of camera modules will initially be reviewed and evaluated based on the amount of information they provide. Using the chosen cameras, we manually set up datasets and annotations, chose and then trained a suitable and appropriate algorithm to predict deviations from an ideal state in plant production. The algorithm chosen was Faster RCNN, which resulted in having a very high detection accuracy. Along with the main type of cameras, a new particular type of images analysis, named SI-NDVI, is done using a particular combination of the main three cameras and the results show that it is able to detect vegetation and able to predict or show if a plant is stressed or not. An in-depth research is done on all these techniques to create a good quality dataset for the purpose of early stress detection. / Olika typer av bilder försedda av olika kombinationer av kameror har kapaciteten att hjälpa öka och optimera odling av växter. Tillsammans med en kraftfull deep learning-modell, för att detektera olika stressindikatorer i RGB bilder, kan signifikant öka skördar. Fältet av AI-lösningar inom jordbruk är inte väl utforskat och denna uppsats siktar på att ta ett första steg i utforskandet av olika tekniker för att detektera tidig stress hos växter. Inom detta arbete kommer olika typer och kombinationer av kameramoduler bli utvärderade baserat på hur mycket information de kan förse. Genom att använda de valda kamerorna skapar vi själva dataseten och kategoriserar dem, därefter välja och träna en lämplig algoritm för att förutspå förändringar från ett idealt tillstånd för växtens tillväxt. Algoritmen som valdes var Faster RCNN, vilken hade en väldigt hög träffsäkerhet. Parallellt med de huvudsakliga kamerorna genomförs en ny typ av bildanalys vid namn SI-NDVI genom användandet av en särskild kombination av de tre kameror och resultat visar att det är möjligt att detektera vegetation och förutspå eller visa om en växt är stressad eller inte. En fördjupad undersökning genomförs på alla dessa tekniker för att skapa ett dataset av god kvalité för att kunna förutspå tidig stress.

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Context: On airport runways, monitoring is done by Precision Runway Monitor (PRM) method with the help of radar. Most of the airports are built near the forests so there is a greater chance of mam-mal intrusion onto the runways leading to massive accidents. At many airports, there are applied old traditional, mostly manual methods in detecting mammals on the runway. Accidents caused by wildlife strikes between aircraft and mammals are increasing day to day, and this is approximately 3%-10% of all reported collisions [1]. We propose a system that monitors the airport runway by detecting mammals. Objectives: The main objective of this project is to investigate and evaluate the possibility of using thermal vision methods to detect the obstacles encountered on the runways. The system should work in real time. Methods: Mammals detection can be done by using a thermal camera with a thermal sensitivity of less than 50mK and a resolution of 640 x 480 pixels. The thermal camera uses an uncooled microbolometer sensor which is lighter, consumes less power and can see through almost all weather conditions like mist, fog, snow etc. Machine Learning based algorithms like background subtraction are used in detecting the mammal, and contours are used to estimate the size and distance. Results: As a result, the mammals moving on the runway can be detected at a distance of up to 400 m. The system estimates a distance of a moving animal and its size with an accuracy of around 90%. Conclusions: A runway monitoring system is needed to prevent wildlife strikes in airports. The proposed system prevents accidents to some extent. However, further tests are required before its commercialisation. There is a need for further quantitative and qualitative validation of the models in full-scale industry trials.

Modern Advanced Driver Assistant Systems (ADAS) focus more on daytime driving and primarily use daylight cameras as the main vision sources to detect, classify, and track objects. However, evidence has proved that autonomous driving using such a setup is compromised in the dark, and consequently, resulting in accidents. The hypothesis is that adding an infrared camera to the existing ADAS will boost the detection rate and accuracy, and further enhance the overall safety. This thesis investigates how well a standalone infrared camera performs onboard vehicle perception tasks such as object detection and classification using both machine learning and deep learning algorithms. Given a custom labeled infrared driving dataset that contains 4 classes of objects, “People”, “Vehicle”, “Bicycle”, and “Animal”, multiple attempts and improvements of training a supervised learning model, namely the linear multi-class Support Vector Machine (SVM) has been made by using various image preprocessing and feature extraction methods to detect the objects. During training, hard example mining is used to reduce the number of false classifications. This SVM employs a One-Against-All (OAA) styled approach and uses the image pyramid technique to enable multi-scale detection. On the deep learning side, a Convolutional Neural Network (CNN) based state-of-the-art detector, the YOLOv4 family including the full-sized and tiny YOLOv4 has been selected, trained, and tested at different input sizes using the same dataset. Labeling format conversion is performed to make this work. The results show that using bilateral filtering with the Histogram of Oriented Gradients (HOG) feature to train an SVM is preferable and is more accurate than the YOLOv4 family. However, the YOLOv4 networks are significantly faster. Overall, a standalone infrared camera cannot provide dominant detection results, but it can definitely supply useful information to the ADAS and complement other sensory devices for improved safety. / Thesis / Master of Applied Science (MASc)

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<p dir="ltr">Polarization and spectral imaging technology has wide application prospects and economic value in environmental detection, target recognition, remote sensing detection and industrial detection. However, the acquisition of hyperspectral or spectro-polarimetric imaging data is difficult and expensive in general. This study aims to develop a synthetic thermal imaging dataset using computer simulation. The study seeks to explore the simulation performance of Monte-Carlo path tracing algorithm in the fields of spectroscopy and thermal imaging. The goal is to provide a novel tool for effective and accurate dataset generation for thermal imaging neural networks training.</p>