Date of Award
Summer 2008
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical & Computer Engineering
Program/Concentration
Electrical Engineering
Committee Director
Zia-ur Rahman
Committee Member
Vijayan Asari
Committee Member
Jiang Li
Call Number for Print
Special Collections LD4331.E55 R355 2008
Abstract
Runway incursion is a persistent problem that has resulted in some of the most devastating accidents in aviation history. With ever increasing air traffic and more passengers, runway safety is of utmost priority to the Federal Aviation Administration (FAA) and other agencies concerned with aviation. As the issue of aviation safety becomes increasingly important, developing a consistent application that detects runway incursions in various visibility conditions is crucial for the aviation industry. This thesis presents a novel method for detecting runway hazards in poor visibility conditions using image processing techniques. The first step is to obtain images of a runway on a clear day and compute the smoothness coefficients. Then edge detection is performed using the SUSAN edge detection algorithm. Finally, database of the smoothness coefficients and edge detected images is developed. For foggy images we compute the smoothness coefficients. Typically, foggy images have low contrast. Hence, before we perform edge detection, we enhance the image using Multi-Scale Retinex (MSR). The MSR provides the improvement in contrast and color constancy that is needed to enhance foggy images, by performing non-linear spatial/spectral transforms. After enhancement, the next step is to run the same edge detection algorithm with appropriate thresholds. Finally, we determine a hazard by comparing the edge detected images of images taken under dear and foggy conditions.
Rights
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DOI
10.25777/h3n1-rf30
Recommended Citation
Rajput, Girish S..
"Image Pre-Processing Techniques for Hazard Detection in Poor Visibility Conditions"
(2008). Master of Science (MS), Thesis, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/h3n1-rf30
https://digitalcommons.odu.edu/ece_etds/497
Included in
Aviation Safety and Security Commons, Electrical and Computer Engineering Commons, Theory and Algorithms Commons