  Many application from the bee colony health state monitoring could be
efficiently solved using a computer vision techniques. One of such challenges
is an efficient way for counting the number of incoming and outcoming bees,
which could be used to further analyse many trends, such as the bee colony
health state, blooming periods, or for investigating the effects of
agricultural spraying. In this paper, we compare three methods for the
automated bee counting over two own datasets. The best performing method is
based on the ResNet-50 convolutional neural network classifier, which achieved
accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2
dataset.
