|
|
Semantic segmentation of aerial images
Pazdera, Jiří ; Králík, Jan (referee) ; Adámek, Roman (advisor)
This work deals with semantic segmentation of aerial images and their subsequent use for route planning. The first part represents an introduction to this issue and a theoretical description of the current state of knowledge. The second part describes testing of available segmentation methods, the development of custom dataset, and the training of an existing neural network model. Finally, the possibility of route planning using an appropriate algorithm is demonstrated.
|
|
|
The use of deep neural networks for the evaluation of metallographic cross-sections
Semančík, Adam ; Mendřický, Radomír (referee) ; Hurník, Jakub (advisor)
Táto diplomová práca skúma aplikáciu hlbokých neurónových sietí pre vylepšenie hodnotenia metalografických výbrusov pre materiály vyrobené pomocou aditívnej výroby. Zameriava sa na dve pokročilé techniky spracovania obrazu: sémantickú segmentáciu a super-rozlíšenie obrazu. Na sémantickú segmentáciu bola použitá architektúra U-Net pre klasifikáciu defektov, ako sú dva typy pórov. Okrem toho bol použitý model SRGAN (Super-Resolution Generative Adversarial Network) pre zvýšenie rozlíšenia obrazu, čo potenciálne zlepšuje presnosť segmentácie. Výskum hodnotí, či model trénovaný na AlSi10Mg môže dostatočne dobre vyhodnocovať materiály Cu99 a Ti6Al4V. Zároveň hodnotí vplyv super-rozlíšenia na výkonnosť segmentácie. Výsledky ukázali, že zatiaľ čo model segmentácie dosahoval dobré výsledky na AlSi10Mg, generalizácia na iné materiály vyžaduje diverzifikovanejšie tréningové dáta. V dôsledku výpočtových obmedzení zostáva kombinovaný efekt super-rozlíšenia a segmentácie nejednoznačný, čo naznačuje potrebu ďalšieho výskumu s výkonnejšími výpočtovými zdrojmi.
|
|
|
Detection of Material Surface Damage Based on a Photograph
Marek, Radek ; Sakin, Martin (referee) ; Dyk, Tomáš (advisor)
This work focuses on the use of various types of neural networks for detecting surface damage of materials from photographs and evaluates their effectiveness. Identifying different types of damage, such as cracks, scratches, and other defects, is essential for assessing the condition of materials and may indicate the need for further maintenance or repairs. The use of advanced neural networks allows for more precise detection and classification of damage, which is crucial for applications in areas such as construction, the automotive industry, and aerospace engineering, where rapid and reliable diagnostics of material defects are critical. Integrating these technologies into regular inspection processes can significantly improve accident prevention and extend the lifespan of structural components. The work also discusses the possibilities for improvement and adaptation of algorithms to specific materials and types of damage. Thus, this work demonstrates how advanced machine learning technologies can significantly contribute to more effective and reliable material condition monitoring, opening paths for future innovations in maintenance and safety.
|
|
|
Semantic segmentation of images from off-road environment
Spilková, Bára ; Králík, Jan (referee) ; Adámek, Roman (advisor)
Hlavním cílem bakalářské práce je prozukoumání růzých metod sémantické segmentace snímků z off-road terénu. V rešeršní části jsou popsány základní principy sémantické segmentace, různé přístupy k tomuto problému, metody sémantické segmentace a různé datové sady. Dále je popsán proces evaluace a trénování několika modelů s rozdílnými parametry a vytvoření nového evaluačního datasetu. Získané výsledky jsou porovnány s výsledky z rešeršní části a jsou navrhnuty další kroky pro zvýšení přesnosti modelů.
|
|
|
Visual Camera Orientation Estimation using Machine Learning
Kubička, Martin ; Polášek, Tomáš (referee) ; Čadík, Martin (advisor)
Účel tejto práce je vytvoriť model pomocou sférických konvolučných neuronových sietí, ktorý vie odhadnúť orientáciu kamery z dvoch vstupov, kde prvým vstupom je panoráma a druhým vstupom je fotka, ktorá zachytáva určitú časť panorámy. Inými slovami, úlohou je nájsť kde v panoráme, ktorá tvorí prvý vstup, sa nachádza fotka, ktorá tvorí druhý vstup. Mimo troch vytvorených modelov, ktoré riešia daný problém, vzniklo aj 6 nových datasetov, ktoré rozširujú momentálne dostupný počet datasetov, ktorých fotky sú v equirectangulárnom alebo stereografickom formáte
|
|
|
Forensic method for recognizing the authenticity of artworks using multispectral analysis
Lánský, David ; Mezina, Anzhelika (referee) ; Burget, Radim (advisor)
Detecting forgeries is crucial for protecting the art market and preserving the authenticity of artworks. This thesis focuses on forgery detection using convolutional neural networks (CNNs). The main goal was to develop advanced methods capable of identifying anomalies, and thus potential forgeries, in images with their X-ray photographs. During this research, U-net architectures and binary semantic segmentation techniques were applied, enabling successful anomaly detection. The main contribution of this work is 112 models of four different U-net and U-net++ architectures, which effectively highlight anomalies through the method of binary semantic segmentation. The models were trained on a set of images with their synthetically created X-ray images and artificially generated anomalies. In this way, the models can detect lead spots, nails, layers of hidden paintings, and other defects, while also being able to ignore insignificant elements, such as picture frames and overexposed X-ray images. The testing of the models occurred in two phases. In the first phase, they were evaluated using the IoU metric on a set of 400 synthetically generated data, where in the best cases, they achieved up to 83.5 % IoU. In the second phase, they were evaluated subjectively on images with real X-rays and natural anomalies. This approach combines traditional X-ray techniques with modern computer vision, revealing deviations that might be overlooked during standard visual inspection. By bridging these technologies, this work opens new possibilities for the protection of art collections and provides a solid foundation for further research in the field of art forgery detection using artificial intelligence.
|
|
|
Semantic Segmentation of Pathologies in Retinal Images
Čabala, Roman ; Orság, Filip (referee) ; Kavetskyi, Andrii (advisor)
The thesis aimed to segment pathology visible in the retina images, such as exudates, hemorrhages, and microaneurysms. For that, two well known deep neural networks, named U-Net and SegFormer, were trained. To test the performance of the models, one publicly available dataset was used, named IDRiD. Obtained results were reported after analyzing different factors which affected the performance of the models U-Net and Segformer.
|
|
|
A convolutional neural network for image segmentation
Mitrenga, Michal ; Petyovský, Petr (referee) ; Jirsík, Václav (advisor)
The aim of the bachelor thesis is to learn more about the problem of convolutional neural networks and to realize image segmentation. This theme includes the field of computer vision, which is used in systems of artificial intelligence. Special Attention is paid to the image segmentation process. Furthermore, the thesis deals with the basic principles of artificial neural networks, the structure of convolutional neural networks and especially with the description of individual semantic segmentation architectures. The chosen SegNet architecture is used in a practical application along with a pre-learned network. Part of the work is a database of CamVid images, which is used for training. For testing, a custom image database is created. Practical part is focused on CNN training and searching for unsuitable parameters for network learning using SW Matlab.
|
|
|
Self-supervised learning in computer vision applications
Vančo, Timotej ; Richter, Miloslav (referee) ; Janáková, Ilona (advisor)
The aim of the diploma thesis is to make research of the self-supervised learning in computer vision applications, then to choose a suitable test task with an extensive data set, apply self-supervised methods and evaluate. The theoretical part of the work is focused on the description of methods in computer vision, a detailed description of neural and convolution networks and an extensive explanation and division of self-supervised methods. Conclusion of the theoretical part is devoted to practical applications of the Self-supervised methods in practice. The practical part of the diploma thesis deals with the description of the creation of code for working with datasets and the application of the SSL methods Rotation, SimCLR, MoCo and BYOL in the role of classification and semantic segmentation. Each application of the method is explained in detail and evaluated for various parameters on the large STL10 dataset. Subsequently, the success of the methods is evaluated for different datasets and the limiting conditions in the classification task are named. The practical part concludes with the application of SSL methods for pre-training the encoder in the application of semantic segmentation with the Cityscapes dataset.
|
|
|
Object Detection in the Laser Scans Using Convolutional Neural Networks
Marko, Peter ; Beran, Vítězslav (referee) ; Veľas, Martin (advisor)
This thesis is aimed at detection of lines of horizontal road markings from a point cloud, which was obtained using mobile laser mapping. The system works interactively in cooperation with user, which marks the beginning of the traffic line. The program gradually detects the remaining parts of the traffic line and creates its vector representation. Initially, a point cloud is projected into a horizontal plane, crating a 2D image that is segmented by a U-Net convolutional neural network. Segmentation marks one traffic line. Segmentation is converted to a polyline, which can be used in a geo-information system. During testing, the U-Net achieved a segmentation accuracy of 98.8\%, a specificity of 99.5\% and a sensitivity of 72.9\%. The estimated polyline reached an average deviation of 1.8cm.
|
guest ::
