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Detekce typu a bodového ohodnocení kartiček ve hře Hobiti
Hlinský, Martin ; Kohút, Jan (referee) ; Vaško, Marek (advisor)
This thesis aims to create a card detector that can train a model that can detect the score of a card and its type using the synthetic generation of the dataset. The YOLOv8 model is used for training. The first step is to take pictures of the cards, which then go through a pre-processing stage so they do not contain background and are aligned. These pre-processed card images are combined with photos from other datasets in a generator that randomly translates, rotates, and otherwise simulates photos of possible card placements. This generator’s output is roughly 50 000 annotated images in the case of the Hobiti game, but different dataset sizes and pre-trained weights are compared in the experiments. The latest generation of trained detectors was validated on a real dataset for unbiased testing, and the most accurate model trained on purely synthetic datasets achieved precision up to 81.5 % according to the 50 metric. It is then possible to implement, for example, a point counter on the final detector, a prototype of which is also described in this paper.
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Synthetic Data Set Generator for Traffic Analysis
Šlosár, Peter ; Juránková, Markéta (referee) ; Herout, Adam (advisor)
This Master's thesis deals with the design and development of tools for generating a synthetic dataset for traffic analysis purposes. The first part contains a brief introduction to the vehicle detection and rendering methods. Blender and the set of scripts are used to create highly customizable training images dataset and synthetic videos from a single photograph. Great care is taken to create very realistic output, that is suitable for further processing in field of traffic analysis. Produced images and videos are automatically richly annotated. Achieved results are tested by training a sample car detector and evaluated with real life testing data. Synthetic dataset outperforms real training datasets in this comparison of the detection rate. Computational demands of the tools are evaluated as well. The final part sums up the contribution of this thesis and outlines some extensions of the tools for the future.
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Synthetic Data Set Generator for Traffic Analysis
Šlosár, Peter ; Juránková, Markéta (referee) ; Herout, Adam (advisor)
This Master's thesis deals with the design and development of tools for generating a synthetic dataset for traffic analysis purposes. The first part contains a brief introduction to the vehicle detection and rendering methods. Blender and the set of scripts are used to create highly customizable training images dataset and synthetic videos from a single photograph. Great care is taken to create very realistic output, that is suitable for further processing in field of traffic analysis. Produced images and videos are automatically richly annotated. Achieved results are tested by training a sample car detector and evaluated with real life testing data. Synthetic dataset outperforms real training datasets in this comparison of the detection rate. Computational demands of the tools are evaluated as well. The final part sums up the contribution of this thesis and outlines some extensions of the tools for the future.
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