Fast detection of bees using deep learning and bayesian optimization
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 |
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