Prepoznavanje jedi iz digitalnih slik s pomočjo konvolucijskih nevronskih mrež

Banko, Jan

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Title:	Prepoznavanje jedi iz digitalnih slik s pomočjo konvolucijskih nevronskih mrež
Authors:	ID Banko, Jan (Author) ID Potočnik, Božidar (Mentor) More about this mentor... ID Šavc, Martin (Comentor)
Files:	UN_Banko_Jan_2018.pdf (2,69 MB) MD5: 58EFC01B17C5C528F908854CD51712CE PID: 20.500.12556/dkum/a71bcc49-752b-483e-9017-c6a71da6a097
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V diplomskem delu se ukvarjamo s prepoznavo jedi iz digitalnih slik s pomočjo konvolucijskih nevronskih mrež. Namen diplomskega dela je razvoj in implementacija sistema, ki je zmožen prepoznati hrano na digitalni sliki. Natančneje smo preučili delovanje konvolucijskih nevronskih mrež ter postopek prepoznavanja objektov. Opisali smo tudi uporabljene algoritme za detekcijo objektov, ki uporabljajo konvolucijske nevronske mreže. Pri implementaciji razpoznavalnika hrane smo se omejili na 8 različnih kategorij hrane. Pri testiranju na podatkovni zbirki »The Food-101 Data Set« je na množici 2400 slik najboljši izmed uporabljenih modelov detektorjev dosegel natančnost prepoznavanja 95,59 % pri uporabi metrike »PASCAL VOC 2010« ter 72,1 % pri uporabi metrike »COCO«.
Keywords:	računalniški vid, prepoznavanje hrane, konvolucijske nevronske mreže, Tensorflow
Place of publishing:	[Maribor
Publisher:	J. Banko
Year of publishing:	2018
PID:	20.500.12556/DKUM-71472
UDC:	004.93:004.032.26(043.2)
COBISS.SI-ID:	21768982
NUK URN:	URN:SI:UM:DK:O14YCFLQ
Publication date in DKUM:	31.08.2018
Views:	3415
Downloads:	321
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	16.08.2018

Secondary language

Language:	English
Title:	Food recognition from digital images using convolutional neural networks
Abstract:	In the thesis we are dealing with food recognition from digital images using convolutional neural networks. The purpose of this thesis is to develop and implement a system, capable of detecting food items in a digital image. We thoroughly studied how convolutional neural networks work, and examined the object detection pipeline. We describe convolutional neural network based object detection algorithms that were used in the thesis. In the implementation of the food detection system we limited ourselves to 8 different food categories. During testing on »The Food-101 Data Set« using 2400 images, the best object detection model of those that were used achieved a classification rate of 95.59 % when using the »PASCAL VOC 2010« metric and 72.1 % when using the »COCO« metric.
Keywords:	computer vision, food recognition, convolutional neural networks, Tensorflow

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