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https://zone.biblio.laurentian.ca/handle/10219/3753
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DC Field | Value | Language |
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dc.contributor.author | Patel, Darshankumar | - |
dc.date.accessioned | 2021-09-07T19:44:24Z | - |
dc.date.available | 2021-09-07T19:44:24Z | - |
dc.date.issued | 2021-08-30 | - |
dc.identifier.uri | https://zone.biblio.laurentian.ca/handle/10219/3753 | - |
dc.description.abstract | In this study, a deep learning model was synthesized for object detection that can give better results than the state-of-the-art accuracy and reliability in production environments making it more accessible to production-ready applications. For this, a model architecture was used that is inspired by state-of-the-art Single Shot Detector architecture. Statistical analysis was performed on the results generated by the model to make final predictions in each frame. Experimental results outperform simple-SSD architecture. For 300 X 300 inputs, it achieves 84.7% mean Average Precision on the system with Nvidia GeForce GTX 1660 Ti 14GB, 16 GB ram, and Intel(R) Core (TM) i7-9750H CPU @ 2.60GHz at 60FPS, outperforming existing architectures of SSD, Faster Region - Convolutional Neural Network, You Only Look Once (YOLO), Resnet50, Resnet100 in use. | en_US |
dc.language.iso | en | en_US |
dc.subject | object detection | en_US |
dc.subject | deep learning | en_US |
dc.subject | statistical modelling | en_US |
dc.subject | SSD | en_US |
dc.subject | FR-CNN | en_US |
dc.subject | Resnet | en_US |
dc.title | Single shot detector for object detection using an ensemble of deep learning and statistical modelling for robot learning applications | en_US |
dc.type | Thesis | en_US |
dc.description.degree | Master of Science (MSc) in Computational Sciences | en_US |
dc.publisher.grantor | Laurentian University of Sudbury | en_US |
Appears in Collections: | Computational Sciences - Master's theses Master's Theses |
Files in This Item:
File | Description | Size | Format | |
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Darshankumar Patel_Final Thesis_Sep_23.pdf | 5.34 MB | Adobe PDF | View/Open |
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