Thesis Details

Využití strojového učení k rozpoznání pohybu feederového prutu

Master's Thesis Student: Vele Patrik Academic Year: 2021/2022 Supervisor: Šimek Václav, Ing.
English title
Exploitation of Machine Learning for Identification of Feeder Rod Movement
Language
Czech
Abstract

The aim of this diploma thesis is to create a device that uses machine learning methods to recognize the movements of a feeder fishing rod based on data from an inertial measurement unit. The introductory part is devoted to the feeder fishing technique, the selection of important movements and the possibilities of attaching the detection device to the rod. This is followed by the creation of a theoretical basis in the field of machine learning, familiarization with the inertial measurement unit and the issue of classification. The acquired knowledge is used to select appropriate techniques for solving the task of recognizing the movements of the rod. In the practical part, a detection device based on the ESP32 platform is designed and created. This is initially used as a motion sensor, which, in combination with the processing of the measured values, serves as a generator of a training data set. The work continues with the implementation of the convolutional neural network, the learning process on the created dataset and the integration of the most successful model into the detection device. The conclusion is devoted to testing in practice, evaluation and possibilities of future development. The result is a small, battery-powered device that, when attached to any feeder rod, provides highly successful detection of all key movements during the hunt. In addition, thanks to wireless communication via ESP-NOW, it is possible to send the results to various devices.

Keywords

Feeder, movement detection, machine learning, Tiny Machine Learning, convolution neural network, embedded system, ESP32, dataset, ESP-NOW, TensorFlow Lite, Google Colaboratory, PlatformIO.

Department
Degree Programme
Files
Status
defended, grade C
Date
17 June 2022
Reviewer
Committee
Kořenek Jan, doc. Ing., Ph.D. (DCSY FIT BUT), předseda
Drábek Vladimír, doc. Ing., CSc. (DCSY FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Lengál Ondřej, Ing., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, Ing., Ph.D. (DCSY FIT BUT), člen
Strnadel Josef, Ing., Ph.D. (DCSY FIT BUT), člen
Citation
VELE, Patrik. Využití strojového učení k rozpoznání pohybu feederového prutu. Brno, 2022. Master's Thesis. Brno University of Technology, Faculty of Information Technology. 2022-06-17. Supervised by Šimek Václav. Available from: https://www.fit.vut.cz/study/thesis/25142/
BibTeX
@mastersthesis{FITMT25142,
    author = "Patrik Vele",
    type = "Master's thesis",
    title = "Vyu\v{z}it\'{i} strojov\'{e}ho u\v{c}en\'{i} k rozpozn\'{a}n\'{i} pohybu feederov\'{e}ho prutu",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2022,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/25142/"
}
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