Counting Instances of Objects in Color Images Using U-Net Network on Example of Honey Bees

This article presents a novel approach to segmentation and counting of objects in color digital images. The objects belong to a certain class, which in this case are honey bees. The authors briefly present existing approaches which use Convolutional Neural Networks to solve the problem of image segmentation and object recognition. The focus however is on application of U-Net convolutional neural network in an environment where knowledge about the object of interest is only limited to its rough, single pixel location. The authors provide full access to the details of the code used to implement the algorithms, as well as the data sets used and results obtained. The results show an encouraging low level of counting error at 14.27% for the best experiment.