Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/173728
Title: A multimodal deep learning approach for food tray recognition
Author: Peracaula Prat, Joan
Director/Tutor: Bolaños, Marc
Radeva, Petia
Keywords: Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Programari
Treballs de fi de grau
Processament digital d'imatges
Visió per ordinador
Aliments
Neural networks (Computer science)
Machine learning
Computer software
Digital image processing
Computer vision
Bachelor's thesis
Food
Issue Date: 13-Sep-2020
Abstract: [en] Food recognition, object detection and classification applied to the food domain, is the main topic of this work. We have studied the problem of recognising food instances in tray images of self-service restaurants and have proposed a novel multimodal deep learning approach. From images and daily menus, the model presented uses two state of the art models in object detection and classification and a multimodal neural network to make significantly refined predictions compared to the baseline object detection model, achieving a class weighted average F1-score of 0.862. An ensemble model built from the proposed and the baseline models, also presented in this work, improves the results achieving a class weighted average F1-score of 0.877.
Note: Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Marc Bolaños i Petia Radeva
URI: http://hdl.handle.net/2445/173728
Appears in Collections:Programari - Treballs de l'alumnat
Treballs Finals de Grau (TFG) - Matemàtiques
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

Files in This Item:
File Description SizeFormat 
codi.zipCodi font3.91 MBzipView/Open
173728.pdfMemòria7.84 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons