Fast detection of bees using deep learning and bayesian optimization
MetadataShow full item record
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.