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dc.contributor.supervisorShafai, Cyrus (Electrical and Computer Engineering)en_US
dc.contributor.authorGaudreau, Colin
dc.date.accessioned2018-04-16T14:07:16Z
dc.date.available2018-04-16T14:07:16Z
dc.date.issued2018
dc.date.submitted2018-04-15T20:41:04Zen
dc.identifier.urihttp://hdl.handle.net/1993/32981
dc.description.abstractIn commercial beekeeping, monitoring the apiaries is difficult as they are often spread over large distances. Building a vision-based hive monitoring system is a promising—albeit difficult—solution to this problem. In this thesis, I approach this task by implementing and training three deep learning based object detection models to detect bees and predators: fast region-based convolutional neural networks (Fast R-CNN), You Only Look Once version 2 (YOLO2), and the single shot multi-box detector (SSD). I also use Bayesian optimization (BO) to tune the detector hyperparameters and test whether it is effective for this task. After training the models and tuning their hyperparameters by hand, I obtained a best F1 score of 0.443 for Fast R-CNN, 0.306 for YOLO2, and 0.428 for SSD on a test dataset. After tuning the hyperparameters using BO, I obtained scores of 0.442 for Fast R-CNN, 0.317 for YOLO2, and 0.459 for SSD.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectComputer visionen_US
dc.subjectObject detectionen_US
dc.subjectMachine learningen_US
dc.subjectBeesen_US
dc.titleFast detection of bees using deep learning and bayesian optimizationen_US
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typemaster thesisen_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.examiningcommitteePawlak, Miroslaw (Electrical and Computer Engineering) Wang, Yang (Computer Science)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteMay 2018en_US


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