Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/100609
Title: Augmented Reality Maintenance Assistant Using YOLOv5
Authors: Malta, Ana
Mendes, Mateus 
Farinha, Torres 
Keywords: Augmented reality; Car engine dataset; Car part detection; Task assistant; YOLOv5
Issue Date: 2021
Project: FCT UIDB/00048/2020 
Serial title, monograph or event: Applied Sciences (Switzerland)
Volume: 11
Issue: 11
Abstract: Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.
URI: http://hdl.handle.net/10316/100609
ISSN: 2076-3417
DOI: 10.3390/app11114758
Rights: openAccess
Appears in Collections:I&D CEMMPRE - Artigos em Revistas Internacionais
I&D ISR - Artigos em Revistas Internacionais

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