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Transformers in Object Detection: A comparative analysis of the effectiveness of transformer-based neural networks in Object Detection tasks
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Orientador(es)
Resumo(s)
Artificial Intelligence is applicable to many different tasks, one of those tasks being Object Detection.
For this task, the most common Machine Learning models are Artificial Neural Networks. While many
Artificial Neural Networks are experimented with, Convolutional Neural Networks are currently the
most common and most developed models that can be used in Object Detection. However, Artificial
Intelligence models that interpret data using different sets of algorithms are still consistently
researched, namely Transformer-based models. In this thesis, Artificial Intelligence models that
integrate Transformer architectures are compared with a state-of-the-art Convolutional Neural
Network, the comparisons being based on three Object Detection tasks: Identifying blueberry batches
in images, identifying balloons in images, and identifying objects represented in the COCO2017
dataset. The first task is the most extensively researched one: it’s a real-world Precision Agriculture
task, and four datasets were annotated from existing footage to train the Object Detection models to
perform this task. This task involves a great deal of experimental work, so the other two tasks serve as
more stable benchmarks: the second task involves an open-source dataset, and for the third task
comparisons were made between already existing results of training these models. Models are
compared based on their predictive power and their training time. This thesis aims to determine
whether Transformer-based Artificial Intelligence models can feasibly compete with state-of-the-art
solution in Object Detection tasks, and, consequently, whether further research on the use of
Transformers for Object Detection can be expected to be fruitful in a practical sense.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Transformers Object Detection Neural Networks Artificial Intelligence Precision Agriculture SDG 2 - Zero hunger