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License Plate Detection Utilizing Synthetic Data from Superimposition

Harrysson, Olof LU (2019) In Master Theses in Mathematical Sciences FMAM05 20191
Mathematics (Faculty of Engineering)
Abstract
Machine learning projects are often constrained by data that is messy, scarce, sensitive or costly to produce. These are issues which could be mitigated by synthetic data.

This thesis tries to improve Swedish license plate localization in images by synthesizing images through superimposition, a process that produces data cheaply and in abundance.

A generative process algorithmically creates images of fake license plates which are glued on top of other images without any context awareness. The resulting images thus contain license plates where they normally don't exist, floating in the sky or across a person's face.

To evaluate the usefulness of this method, a machine learning algorithm called YOLOv3 is trained on synthesized data... (More)
Machine learning projects are often constrained by data that is messy, scarce, sensitive or costly to produce. These are issues which could be mitigated by synthetic data.

This thesis tries to improve Swedish license plate localization in images by synthesizing images through superimposition, a process that produces data cheaply and in abundance.

A generative process algorithmically creates images of fake license plates which are glued on top of other images without any context awareness. The resulting images thus contain license plates where they normally don't exist, floating in the sky or across a person's face.

To evaluate the usefulness of this method, a machine learning algorithm called YOLOv3 is trained on synthesized data and tested on real images containing Swedish license plates.

Our findings show that training on synthesized data by itself is almost good enough to match the performance of training on publicly available real data containing international license plates. The best results are achieved by mixing real and synthetic data. (Less)
Popular Abstract (Swedish)
Artificiell intelligens (AI) har på senare tid fått en otrolig uppmärksamhet. Det tröttsamma arbetet med att manuellt koda in lösningar i ett program har ersatts av att lära programmen att hitta egna lösningar; detta är en AI-teknik som kallas för maskininlärning.

Sådana program tränar upp förmågan att känna igen mönster eller anomalier i data, vilket kan användas för allt från att köra bilar till att spela datorspel.
Please use this url to cite or link to this publication:
author
Harrysson, Olof LU
supervisor
organization
course
FMAM05 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
AI, machine learning, deep learning, CycleGAN, YOLOv3
publication/series
Master Theses in Mathematical Sciences
report number
LUTFMA-3380-2019
ISSN
1404-6342
other publication id
2019:E18
language
English
id
8977867
date added to LUP
2019-07-15 10:03:28
date last changed
2019-07-15 10:32:14
@misc{8977867,
  abstract     = {{Machine learning projects are often constrained by data that is messy, scarce, sensitive or costly to produce. These are issues which could be mitigated by synthetic data.

This thesis tries to improve Swedish license plate localization in images by synthesizing images through superimposition, a process that produces data cheaply and in abundance.

A generative process algorithmically creates images of fake license plates which are glued on top of other images without any context awareness. The resulting images thus contain license plates where they normally don't exist, floating in the sky or across a person's face.

To evaluate the usefulness of this method, a machine learning algorithm called YOLOv3 is trained on synthesized data and tested on real images containing Swedish license plates.

Our findings show that training on synthesized data by itself is almost good enough to match the performance of training on publicly available real data containing international license plates. The best results are achieved by mixing real and synthetic data.}},
  author       = {{Harrysson, Olof}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master Theses in Mathematical Sciences}},
  title        = {{License Plate Detection Utilizing Synthetic Data from Superimposition}},
  year         = {{2019}},
}