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Asplund, C. (2016) Object classification and localization using machine learning techniques. Göteborg : Chalmers University of Technology
BibTeX
@mastersthesis{
Asplund2016,
author={Asplund, Carl},
title={Object classification and localization using machine learning techniques},
abstract={When working with object classification and localization in image data, the development
of traditional rule-based solutions has stagnated in recent years. In its place, machine
learning has become a major field of research in order to handle more and more complex
image recognition problems. With machine learning, new state-of-the-art models can be
developed by training a model instead of implementing an explicitly programmed feature
detector.
In this thesis, a literature study covering the field of machine learning has been carried
out on behalf of Volvo Advanced Technology and Research. Furthermore, with an autonomous
garbage handling project initiated by Volvo in mind, two machine learning
models meant for limited hardware-deployment have been designed and trained. The
classification model is based on knowledge distillation, where a compact model learns to
generalize from a more complex state-of-the-art model, and a localization model, where
a typical machine learning implementation is combined with computer vision solutions
from the OpenCV framework.
Both models, that were trained on images from the ImageNet database, produced poor
results in their respective tasks. The process of knowledge distillation, used to train the
classifier, was not achievable due to unfortunate choice of cumbersome model combined
with hardware limitations during training. The hardware was also an issue for the localization
model, which due to this and unwanted performance from the OpenCV corner
detector converged early during training and ended up producing unchanged results for
different input. However, the thesis as a whole came to important conclusions regarding
a proper next step in order to stay competitive within the field of machine learning.},
publisher={Institutionen för fysik (Chalmers), Chalmers tekniska högskola},
place={Göteborg},
year={2016},
keywords={machine learning, computer vision, classification, localization, neural networks,},
note={53},
}
RefWorks
RT Generic
SR Electronic
ID 239156
A1 Asplund, Carl
T1 Object classification and localization using machine learning techniques
YR 2016
AB When working with object classification and localization in image data, the development
of traditional rule-based solutions has stagnated in recent years. In its place, machine
learning has become a major field of research in order to handle more and more complex
image recognition problems. With machine learning, new state-of-the-art models can be
developed by training a model instead of implementing an explicitly programmed feature
detector.
In this thesis, a literature study covering the field of machine learning has been carried
out on behalf of Volvo Advanced Technology and Research. Furthermore, with an autonomous
garbage handling project initiated by Volvo in mind, two machine learning
models meant for limited hardware-deployment have been designed and trained. The
classification model is based on knowledge distillation, where a compact model learns to
generalize from a more complex state-of-the-art model, and a localization model, where
a typical machine learning implementation is combined with computer vision solutions
from the OpenCV framework.
Both models, that were trained on images from the ImageNet database, produced poor
results in their respective tasks. The process of knowledge distillation, used to train the
classifier, was not achievable due to unfortunate choice of cumbersome model combined
with hardware limitations during training. The hardware was also an issue for the localization
model, which due to this and unwanted performance from the OpenCV corner
detector converged early during training and ended up producing unchanged results for
different input. However, the thesis as a whole came to important conclusions regarding
a proper next step in order to stay competitive within the field of machine learning.
PB Institutionen för fysik (Chalmers), Chalmers tekniska högskola,
LA eng
LK http://publications.lib.chalmers.se/records/fulltext/239156/239156.pdf
OL 30
