Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions
Visibility is an important part of highway driving. Weather-related visibility limitations have caused several major pile-ups (crashes) over the years all over the country. These limitations are caused due to, but not limited to, fog and rain. It is vital to be aware of the d...
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ndltd-fsu.edu-oai-fsu.digital.flvc.org-fsu_4056192020-06-24T03:07:50Z Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions Patel, Jigar Kamlesh (authoraut) DeBrunner, Victor (professor directing thesis) Meyer-Baese, Uwe H. (committee member) Harvey, Bruce A. (committee member) Florida State University (degree granting institution) College of Engineering (degree granting college) Department of Electrical and Computer Engineering (degree granting departmentdgg) Text text Florida State University Florida State University English eng 1 online resource (118 pages) computer application/pdf Visibility is an important part of highway driving. Weather-related visibility limitations have caused several major pile-ups (crashes) over the years all over the country. These limitations are caused due to, but not limited to, fog and rain. It is vital to be aware of the driving environment and conditions when traveling on interstates and highways at speeds upwards of 70 mph. Crashes caused at these speeds usually result in injuries and sometimes death. In an effort to minimize/reduce the rate of such incidents, this project intends on automatically detecting driving visibility ranges in real time based on camera systems that already exist on the Florida Highway System. This will be done to alert travelers of driving hazards due to visibility limitations. We plan on doing this by performing analysis on images taken over time to detect the density of fog to determine the visibility distance. This thesis presents a conceptual development, theoretical design, implementation, and the implementation results to achieve automatic fog detection in day-time and night-time images on a standard scale regardless of the image intensity. (i.e. Day-time and Night-time images have the same scale). Images are considered independently with no reference image to compare with. A review of related work is conducted for fog detection and sharpness measure of images to achieve this goal. A Thesis submitted to the Department of Computer & Electrical Engineering in partial fulfillment of the requirements for the degree of Master of Science. Fall Semester 2016. September 09, 2016. Fog detection Includes bibliographical references. Victor DeBrunner, Professor Directing Thesis; Uwe Meyer-Baese, Committee Member; Bruce Harvey, Committee Member. Electrical engineering FSU_FA2016_Patel_fsu_0071N_13465 http://purl.flvc.org/fsu/fd/FSU_FA2016_Patel_fsu_0071N_13465 This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. http://diginole.lib.fsu.edu/islandora/object/fsu%3A405619/datastream/TN/view/Automatic%20Fog%20Detection%20in%20Day%20and%20Night%20Images%20to%20Improve%20Highway%20Driving%20Conditions.jpg |
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Electrical engineering Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions |
description |
Visibility is an important part of highway driving. Weather-related visibility limitations have caused several major pile-ups
(crashes) over the years all over the country. These limitations are caused due to, but not limited to, fog and rain. It is vital to be
aware of the driving environment and conditions when traveling on interstates and highways at speeds upwards of 70 mph. Crashes caused at
these speeds usually result in injuries and sometimes death. In an effort to minimize/reduce the rate of such incidents, this project
intends on automatically detecting driving visibility ranges in real time based on camera systems that already exist on the Florida
Highway System. This will be done to alert travelers of driving hazards due to visibility limitations. We plan on doing this by performing
analysis on images taken over time to detect the density of fog to determine the visibility distance. This thesis presents a conceptual
development, theoretical design, implementation, and the implementation results to achieve automatic fog detection in day-time and
night-time images on a standard scale regardless of the image intensity. (i.e. Day-time and Night-time images have the same scale). Images
are considered independently with no reference image to compare with. A review of related work is conducted for fog detection and
sharpness measure of images to achieve this goal. === A Thesis submitted to the Department of Computer & Electrical Engineering in partial fulfillment
of the requirements for the degree of Master of Science. === Fall Semester 2016. === September 09, 2016. === Fog detection === Includes bibliographical references. === Victor DeBrunner, Professor Directing Thesis; Uwe Meyer-Baese, Committee Member; Bruce Harvey,
Committee Member. |
author2 |
Patel, Jigar Kamlesh (authoraut) |
author_facet |
Patel, Jigar Kamlesh (authoraut) |
title |
Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions |
title_short |
Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions |
title_full |
Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions |
title_fullStr |
Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions |
title_full_unstemmed |
Automatic Fog Detection in Day and Night Images to Improve Highway Driving Conditions |
title_sort |
automatic fog detection in day and night images to improve highway driving conditions |
publisher |
Florida State University |
url |
http://purl.flvc.org/fsu/fd/FSU_FA2016_Patel_fsu_0071N_13465 |
_version_ |
1719323319223713792 |