Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.00 vteřin. 
Classification of Radar Detections Using Convolutional Neural Networks
Láníček, Adam ; Zemčík, Pavel (oponent) ; Maršík, Lukáš (vedoucí práce)
The goal of this thesis was to create an object recognition pipeline for millimeter wave radar data. The work presents a mechanism for encoding the radar data into images as well as an in-house developed annotation tool to facilitate the dataset creation for the You Only Look Once (YOLO) based object recognition models. The YOLO detector trained on a cycling route dataset reported 91% accuracy. This solution, therefore, provides a proof of concept that can be further developed to improve the detection capabilities or to meet the requirements of the specific use cases and environments.
Classification of Radar Detections Using Convolutional Neural Networks
Láníček, Adam ; Zemčík, Pavel (oponent) ; Maršík, Lukáš (vedoucí práce)
The goal of this thesis was to create an object recognition pipeline for millimeter wave radar data. The work presents a mechanism for encoding the radar data into images as well as an in-house developed annotation tool to facilitate the dataset creation for the You Only Look Once (YOLO) based object recognition models. The YOLO detector trained on a cycling route dataset reported 91% accuracy. This solution, therefore, provides a proof of concept that can be further developed to improve the detection capabilities or to meet the requirements of the specific use cases and environments.

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