Honey bees are of particular interest to research in computer assisted behavioral analysis because of their complex social behavior and the large number of individuals in a colony. An important part for such studies is the detection and localization of bees during their activities in the hive. This thesis specifically focuses on bees located in honeycomb cells. A ground truth dataset was created by manually marking bees located inside cells in images of  whole honeycombs. Detail images of the marked position and random, unmarked positions were respectively extracted as positive and negative examples. A convolutional neural network was designed and trained on this dataset to classify detail images on whether they show bees in cells or not. The quality of the training was evaluated and heatmaps were created to visualize prediction strength in the original images. Local maxima of the heatmaps were determined as the predicted positions of bees in honeycomb cells. Several modifications were made during the  work to increase the precision of the network predictions. Testing the classifier used on the test sets made it possible to achieve a detection rate of 1 while testing on detail images  and a detection rate of about 0.6 while testing on images of an entire honeycomb.