Classificação e rastreamento de itens em uma esteira móvel utilizando redes convolucionais e processamento de imagens
Given the prosperity of the modern world, there is a growing need to reduce the time spent on trivial chores. In the context of buying groceries, recent studies point out that one of the most relevant factors on the buyer’s experience, that reflects on sales and revenue, is the time spent in queu...
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| Formato: | bachelorThesis |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/50678 |
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| Resumo: | Given the prosperity of the modern world, there is a growing need to reduce the time
spent on trivial chores. In the context of buying groceries, recent studies point out that one
of the most relevant factors on the buyer’s experience, that reflects on sales and revenue,
is the time spent in queues. The work in question aims to describe the creation of a
computer vision and deep learning prototype, to be installed next to a camera suspended
on a mobile supermarket conveyor belt. It will be responsible for detecting, classifying,
tracking and counting of all passing items. The video stream is processed in real time,
and upon detecting the passage of a specific item, the final purchase bill is increased. As
there would be no human interference, the process tends to simplify, make cheaper and
speed up supermarket checkouts. Among the technologies explored is “state of the art”
convolutional neural networks (CNN), especially YOLO v4 tiny and YOLO v5 small, as
well as some more consolidated ones such as OpenCV for image processing or Roboflow
for database augmentation. At the end of the experiment, it was possible to develop
a model that had up to 77% of average precision (mAP@[0.5:0.95]) for two items on a
treadmill, using a model trained in a hybrid dataset, composed of images collected in vitro
and images generated through a simulator, in addition to a graphical interface responsible
for viewing the processed video feed, which also allows manipulation of hyperparameters
from the CNN, tracker and item counter. |
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