Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Object Detection in Infrared Images using Deep Convolutional Neural Networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the master thesis about object detection(OD) using deep convolutional neural network(DCNN), the area of OD is being tested when being applied to infrared images(IR). In this thesis the, goal is to use both long wave infrared(LWIR) images and short wave infrared(SWIR) images taken from an airplane in order to train a DCNN to detect runways, Precision Approach Path Indicator(PAPI) lights, and approaching lights. The purpose for detecting these objects in IR images is because IR light transmits better than visible light under certain weather conditions, for example, fog. This system could then help the pilot detect the runway in bad weather. The RetinaNet model architecture was used and modified in different ways to find the best performing model. The models contain parameters that are found during the training process but some parameters, called hyperparameters, need to be determined in advance. A way to automatically find good values of these hyperparameters was also tested. In hyperparameter optimization, the Bayesian optimization method proved to create a model with equally good performance as the best performance acieved by the author using manual hyperparameter tuning. The OD system was implemented using Keras with Tensorflow backend and received a high perfomance (mAP=0.9245) on the test data. The system manages to detect the wanted objects in the images but is expected to perform worse in a general situation since the training data and test data are very similar. In order to further develop this system and to improve performance under general conditions more data is needed from other airfields and under different weather conditions.

Place, publisher, year, edition, pages
2018. , p. 43
Series
UPTEC F, ISSN 1401-5757 ; 18028
Keywords [en]
Object detection, Infrared images, Deep Convolutional Neural Network
Keywords [sv]
Objektdetektering, Infraröda bilder, neurala nätverk
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-355221OAI: oai:DiVA.org:uu-355221DiVA, id: diva2:1228617
External cooperation
Saab Avionic Systems, Engineering Department, Video & Graphics
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2018-07-03 Created: 2018-06-28 Last updated: 2018-07-03Bibliographically approved

Open Access in DiVA

fulltext(5657 kB)252 downloads
File information
File name FULLTEXT01.pdfFile size 5657 kBChecksum SHA-512
20d5aa2274a97976b9d9588bbfa2d15458ddcd2b20cf555af6017807d79ecae35d3309c5ae9da90419c9c9c08d6fe5687f19ada34e969c9bd2a20339bd868f8a
Type fulltextMimetype application/pdf

By organisation
Division of Systems and Control
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 252 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 130 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf