dc.contributor.author	Gaudreau, Colin
dc.contributor.examiningcommittee	Pawlak, Miroslaw (Electrical and Computer Engineering) Wang, Yang (Computer Science)	en_US
dc.contributor.supervisor	Shafai, Cyrus (Electrical and Computer Engineering)	en_US
dc.date.accessioned	2018-04-16T14:07:16Z
dc.date.available	2018-04-16T14:07:16Z
dc.date.issued	2018
dc.date.submitted	2018-04-15T20:41:04Z	en
dc.degree.discipline	Electrical and Computer Engineering	en_US
dc.degree.level	Master of Science (M.Sc.)	en_US
dc.description.abstract	In 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.description.note	May 2018	en_US
dc.identifier.uri	http://hdl.handle.net/1993/32981
dc.language.iso	eng	en_US
dc.rights	open access	en_US
dc.subject	Computer vision	en_US
dc.subject	Object detection	en_US
dc.subject	Machine learning	en_US
dc.subject	Bees	en_US
dc.title	Fast detection of bees using deep learning and bayesian optimization	en_US
dc.type	master thesis	en_US

Fast detection of bees using deep learning and bayesian optimization

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