  The Varroa destructor mite is one of the most dangerous Honey Bee (Apis
mellifera) parasites worldwide and the bee colonies have to be regularly
monitored in order to control its spread. Here we present an object detector
based method for health state monitoring of bee colonies. This method has the
potential for online measurement and processing. In our experiment, we compare
the YOLO and SSD object detectors along with the Deep SVDD anomaly detector.
Based on the custom dataset with 600 ground-truth images of healthy and
infected bees in various scenes, the detectors reached a high F1 score up to
0.874 in the infected bee detection and up to 0.727 in the detection of the
Varroa Destructor mite itself. The results demonstrate the potential of this
approach, which will be later used in the real-time computer vision based honey
bee inspection system. To the best of our knowledge, this study is the first
one using object detectors for this purpose. We expect that performance of
those object detectors will enable us to inspect the health status of the honey
bee colonies.
