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Web Application for Inspecting Results of Automatic Video Processing and Manual Annotations
Červíček, Petr ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
The thesis pursues the implementation of the web application for obtaining valuable data for anotation. Data are mainly collected from videos and images, but they can be obtained from deepfakes images and gifs as well. The YOLO - You Only Look Once is used for object detection in the videos. Whole application is developed in node.js and react. Node.js for backend and react for frontend. For a better understanding of web application, there is also historical description of the web technologies.
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Automatic Trafic Scene Analysis Using Image Processing
Válek, Lukáš ; Špaňhel, Jakub (referee) ; Zemčík, Pavel (advisor)
Tato práce se zabývá problematikou analýzy scény pomocí metod počítačového vidění. Cílem této práce je vytvořit systém schopný automaticky detekovat anomálie nacházející se ve video záznamech. Práce se zabývá systémy pro detekci a sledování objektů v obraze, tvorbou grafického uživatelského rozhraní a algoritmem pro detekci porušení uživatelem definovaných pravidel. Výsledkem práce je webová aplikace, která uživateli umožňuje správu videozáznamů, definování pravidel pro scénu, zahájení detekce anomálií a zobrazení výsledků analýzy. Systém pracuje v reálném čase, upozorňuje uživatele o dokončení operace a uchovává výsledky analýzy pro další zpracovaní.
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Deep-learning-based pattern detection in medical images
Koščová, Zuzana ; Vičar, Tomáš (referee) ; Jakubíček, Roman (advisor)
This Bachelor thesis deals with Deep-learning-based pattern detection in medical images. For better understanding of a subject artificial neural network and convolutional neural network (CNN) are described at first. Next chapter is focused on specific detection methods which use CNN. Within a bachelor thesis a dataset of abdominal CT a MRI scans was created. Faster R-CNN and YOLO algorithms were trained and tested on acquired scans for liver detection. Implementation of chosen methods took place in Python programming language using the Pytorch library. Finally, detection results and possible use in medicine are discussed.
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Detection of Traffic Signs and Lights
Chocholatý, Tomáš ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The thesis focuses on traffic sign detection and traffic lights detection in view with utilization convolution neural network. The goal is create suitable detector for detection and classification traffic sign in real traffic. For training of convolution neural network were created appropriate datasets, that contains synthetic and real dataset. For synthetic dataset was create generator, that can simulated different deformation of traffic signs. Evaluation is done by own program for quantitative evaluation. The detection rate successfully detected signs is 89\% over own test dataset. The results allow to find out importance of representation real or synthetic dataset in training dataset and influence individual deformations synthetic dataset for final detection quality.
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Detection of Vehicle License Plates in Video
Líbal, Tomáš ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis deals with preparation of training dataset and training of convolutional neural network for licence plate detection in video. Darknet technology was used for detection, specifically the YOLOv3-tiny neural network model. The solution was focused on the most accurate detection and the smallest number of false positives per image, thus minimizing overall model error. Dataset was prepared from existing freely available datasets, from the dataset provided by the GRAPH@FIT research group, and from self-annotated images created from downloaded YouTube videos. Furthermore, this dataset has been processed using data augmentation, extending it to twice the size. The YOLO Mark tool was used to create annotations. An ROC curve was used to visualize the detection success. Created solution reaches minimum total error 10,849%. Part of the solution is already mentioned dataset.
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Deep Learning for Object Detection
Pitoňák, Radoslav ; Dobeš, Petr (referee) ; Teuer, Lukáš (advisor)
This thesis analyzes different object detection methods which are based on deep neural networks. In the beginning, the convolutional neural networks are described and commonly used object detection methods are compared. In the following parts, the proposal and implementation of the object detection model trained on the specific dataset are described. In conclusion, the achieved results of this model are discussed and compared with the results of other methods.
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Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python
Voronin, Artyom ; Appel, Martin (referee) ; Bastl, Michal (advisor)
This thesis introduces the issue of data classification in the image using tools for machine learning in Python. The aim is to verify the possibilities of overtraining existing models on their own data and evaluating the efficiency and complexity of the entire process. Subsequently, the processing of the achieved results in the form of a demonstration task, image capturing by a web camera and classification of the object in the field of view.
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Detection of Traffic Signs in Image and Video
Kočica, Filip ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis deals with the traffic sign detection problematics using modern techniques in image processing. Special architecture of deep convolutional neural network YOLO, i.e. You Only Look Once, which performs both detection and classification in one step, has been used. This architecture allows object detector to work on very high speeds. This thesis also deals with comparison of models trained on real and synthetic datasets. The best model trained on real dataset has reached 63.4% mAP success rate and 82.3% mAP when trained on synthetic dataset. Evaluation of one image takes about ~40.4ms on average graphics processing unit and ~3.9ms on higher than average graphics processing unit. The benefit of this thesis is that under certain conditions neural network model trained on synthetic data can achieve same or even better results than model trained on real data. This may simplify process of object detector development since it is not necessary to annotate large number of images.
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Detector of the Human Head in Image
Svoboda, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Detection of human head is an important part of person detection and identification algorithms. This thesis is focused on the detection of human head with methods based on neural networks. The majority the of conventional detectors can identify objects within a limited range of positions, whereas models based on neural networks offer a more robust approach. In this thesis we trained the current state-of-the-art models and compared their accuracy and speed. The most accurate model proved to be RetinaNet which has reached 85.15% AP. This detector can be used to improve current available algorithms for person detection, identification and tracking.
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Visual tracking systém pro UAV
KOLÁŘ, Michal
This master thesis deals with the analysis of the current possibilities for object tracking in the image, based on which is designed a procedure for creating a system capable of tracking an object of interest. Part of this work is designing virtual reality for the needs of implementation of the tracking system, which is finally deployed and tested on a real prototype of unmanned vehicle.
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