Assessing Thermal Imagery Integration into Object Detection Methods on Air-Based Collection Platforms

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dc.contributor.advisorOughton, Edward
dc.creatorGallagher, James
dc.date2023-05-03
dc.date.accessioned2023-06-19T12:50:06Z
dc.date.available2023-06-19T12:50:06Z
dc.description.abstractObject detection models commonly focus on utilizing the visible spectrum via Red-Green-Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) (7.5 - 13.5 μm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and RGB-LWIR fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation finding that a blended RGB-LWIR model generally exhibits superior performance compared to traditional RGB or LWIR approaches. For example, an RGB-LWIR blend only performed 1-5% behind the RGB approach in predictive power across various altitudes and periods of clear visibility. Yet, RGB fusion with a thermal signature overlayed provides edge redundancy and edge emphasis, both which are vital in supporting edge detection machine learning algorithms.
dc.format.mediummasters theses
dc.identifier.urihttps://hdl.handle.net/1920/13373
dc.language.isoen
dc.rightsCopyright 2023 James Gallagher
dc.rights.urihttps://rightsstatements.org/vocab/InC/1.0
dc.subject.keywordsThermal object detection
dc.subject.keywordsLong Wave Infrared (LWIR)
dc.subject.keywordsUncrewed Arial Systems (UAS)
dc.subject.keywordsMachine learning
dc.titleAssessing Thermal Imagery Integration into Object Detection Methods on Air-Based Collection Platforms
dc.typeText
thesis.degree.disciplineGeoinformatics and Geospatial Intelligence
thesis.degree.grantorGeorge Mason University
thesis.degree.levelMaster's
thesis.degree.nameMaster of Science in Geoinformatics and Geospatial Intelligence

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