Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp01p5547v675
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Heide, Felix | - |
dc.contributor.author | McManamon, Brendan | - |
dc.date.accessioned | 2023-08-08T12:22:00Z | - |
dc.date.available | 2023-08-08T12:22:00Z | - |
dc.date.created | 2023-04-12 | - |
dc.date.issued | 2023-08-08 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp01p5547v675 | - |
dc.description.abstract | Object detection based on RGB images alone often suffers due to a lack of illumination or other environmental conditions, while thermal infrared cameras generally succeed in those exact challenging scenarios. In the realm of RGB-thermal sensor fusion, previous research has been conducted using a variety of network architectures and fusion techniques. Using a YOLOv7 architecture, this work uses pixel-level early fusion and ensemble late fusion to compare against single-sensor models, trained and evaluated on the Teledyne FLIR dataset. Results indicate that pixel-level fusion significantly outperforms RGB and thermal models while maintaining latency in the 8 ms range on a Tesla V100 with an overall mAP of 92.8%, while the ensemble approach performed below baseline and more than doubled latency. | en_US |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en_US |
dc.title | RGB-Thermal Fusion for Improved Object Detection | en_US |
dc.type | Princeton University Senior Theses | |
pu.date.classyear | 2023 | en_US |
pu.department | Electrical and Computer Engineering | en_US |
pu.pdf.coverpage | SeniorThesisCoverPage | |
pu.contributor.authorid | 920228126 | |
pu.mudd.walkin | No | en_US |
Appears in Collections: | Electrical and Computer Engineering, 1932-2023 |
Files in This Item:
File | Description | Size | Format | |
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MCMANAMON-BRENDAN-THESIS.pdf | 5.6 MB | Adobe PDF | Request a copy |
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