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Pedestrians Detection in Traffic Environment by Machine Learning
 Tilgner, Martin ; Klečka, Jan (oponent) ; Horák, Karel (vedoucí práce)
This thesis deals with pedestrian detection using convolutional neural networks from the perspective of autonomous vehicle. Especially by testing these networks in the sense of finding a suitable practice of creating a dataset for machine learning models. A total of ten machine learning models of meta architectures Faster R-CNN with ResNet 101 as a feature extractor and SSDLite with the MobileNet_v2 feature extractor were trained. These models were trained on datasets of various sizes. The best results were achieved on a dataset of 5 000 images. In addition to these models, a new dataset aimed at pedestrians at night was created. Furthermore, a Python library was created for work with datasets and script for automatic creation of dataset.
Úplný záznam
Pedestrians Detection in Traffic Environment by Machine Learning
 Tilgner, Martin ; Klečka, Jan (oponent) ; Horák, Karel (vedoucí práce)
This thesis deals with pedestrian detection using convolutional neural networks from the perspective of autonomous vehicle. Especially by testing these networks in the sense of finding a suitable practice of creating a dataset for machine learning models. A total of ten machine learning models of meta architectures Faster R-CNN with ResNet 101 as a feature extractor and SSDLite with the MobileNet_v2 feature extractor were trained. These models were trained on datasets of various sizes. The best results were achieved on a dataset of 5 000 images. In addition to these models, a new dataset aimed at pedestrians at night was created. Furthermore, a Python library was created for work with datasets and script for automatic creation of dataset.
Úplný záznam

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